Nowadays, automatic tumor detection in Magnetic Resonance Image (MRI) is very important in many diagnostic and therapeutic applications due to their unpredictable shape and appearance. In medical image analysis, the automatic segmentation of brain tumors from MRI data is one of the most critical issues. The existing researches have some limitations like high processing time and less accuracy, because of their time consumption of training process. In this research, a new automatic segmentation process is introduced by using machine learning and swarm intelligence scheme. Here, a Fuzzy Logic with Spiking Neuron Model (FL-SNM) is proposed for segmenting the brain tumor region from MRI. Initially, the input images are pre-processed to remove Gaussian and poison noise by using Modified Kuan Filter (MKF). In MKF, the optimal selection of minimum MSE of image pixels is done by using Random Search Algorithm (RSA) which improves the Peak-Signal-Noise-Ratio (PSNR). Then, the image is smoothing by Anisotropic Diffusion Filter (ADF) to reduce over-filtering problem. Afterward, Fisher’s Linear-Discriminant Analysis is used to extract the statistical texture features. Finally, the extracted features are transferred to FL-SNM process and this scheme effectively segments the tumor region. In FL-SNM, the consequent parameters like weight and bias place important role to segment the region. So, the weight parameter values are optimized by using Chicken behavior based Swarm Intelligence (CSI) algorithm is proposed. The proposed (FL-SNM) scheme has attain better performance in terms of high accuracy rate of 94.87% sensitivity rate of 92.07%, specificity rate of 99.34%,precision rate of 89.36%, recall rate of 88.39% F-measure rate of 95.06%, G-mean rate of 95.63%, and DSC rate of 91.2% compared than existing Convolutional Neural Networks (CNN) and Hierarchical Self Organizing Map (HSOM).

ABSTRACT\n Nowadays, the Economic Dispatch (ED) problem is the determination of generation levels such that the total cost of generation becomes minimum for a defined level of load. Therefore one needs to consider the fuel cost characteristics of the generators while finding their optimal real power outputs. Economic Load Dispatch (ELD) is essentially a cost minimization problem in thermal/wind power generation. In order to achieve better optimization results, in this paper, a Lévy-flight Krill Herd (LKH) algorithm with Quantum Search Algorithm (QSA) for global optimization is proposed for solving optimization tasks within limited computing time and less cost. To improve the performance of LKH, a new Local Lévy-Flight (LLF) operator is introduced, during the process of updating krill in order to improve its efficiency and reliability coping with global numerical optimization issues. The LLF operator supports the exploitation and can makes the krill individuals search the space carefully at the end of the search. Then, the elitism scheme is applied to keep the best krill during the process of updating the krill. The result comparisons have shown that the proposed methods are highly effective for solving ELD problem with multiple fuel options compared with existing results.

Weed management in rice continues to be a major challenge to the success of rice growers in northern Iran. Field experiments were conducted at Sari Agricultural Sciences and Natural Resources University to investigate the spatial distribution of weed seeds in the rice growth cycle in 2010 and 2011. Transplanting was done on June 6 in both years. Samples for seedbank analysis were collected 10 days before transplanting and emerged weed density was determined on three different dates during the growing season. Results indicated that nutsedge (Cyperus spp) and bahiagrass (Paspalum notatum) were the two most abundant weed species. The vertical distribution of weed seeds decreased by depth from 10 to 30 cm, while weed pressure was the highest at the 0-10 cm soil depth. There was no relationship between soil weed seedbanks (at different depths) and emerged weed populations, suggesting that weed seedbank data are not good predictors of weed seedling densities. Nevertheless, Kriging maps indicated that the spatial distribution of weed seeds was in accordance with seedling germination pattern. Also the regression coefficient for 0-10 cm soil depth was R2=0.17 and R2=0.34 for relation between nutsedge and bahiagrass seedlings and their seedbank in 2010 and also, R2=0.18 and R2=0.05 in 2011, respectively. Therefore, results achieved from this depth can be used to predict the relationship between nutsedge and bahiagrass seedlings densities and weed seedbanks. The results of this study provide an option for the farmers growing rice to understand the dynamics of weed populations in a cost effective way.

Legumes have been used for their nitrogen fixation properties; however, farmers in tropical countries have not used it to a great extent, because they do not appreciate visible improvement in the soil. The aim of this paper was to analyze the effect of three tropical legumes (Phaseolus vulgaris, Canavalia ensiformis and Clitoria ternatea) on growth - promoting microorganisms, [Free Life Nitrogen Fixers (FLNF), Azospirillum sp., Azotobacter sp. and mycorrhiza on the fertility of two rhizospheric soils, one for livestock use (Lu) and other for agricultural use (Au). The bioassays were established under a completely randomized design with three replicates per species. The evaluated soil properties were: pH; Organic matter, OM; Total carbon, CT; Total nitrogen, N; Useful Phosphorus, P; Cation Exchange Capacity, CEC and texture. In rhizospheric soil the populations of FLNF, Azospirillum sp. and Azotobacter sp., as well as mycorrhizal spores were estimated. The results indicate that Phaseolus vulgaris was the species that showed lower N fixation in Au soil; but higher P content was found in Lu soil. Canavalia ensiformis and Clitoria ternatea had higher N fixation, increased CEC, OM and TC. Clitoria ternatea favored the accumulation of OM and CT, promoting CEC, pH and and the mycorrhizal population. Canavalia ensiformis was the only species to promote differentiated development of Azospirillum sp. and Azotobacter sp. in Lu soil, showing higher populations with this legume. Therefore, it is recommended that these data can be considered for the conservation of tropical species, both legumes and native microorganisms.

In this paper, An Efficient and Reliable Core-Assisted Multicast Routing Protocol (ERASCA) is secured from a malicious/selfish receiver through battery estimation technique. In ERASCA, a core node will be responsible for the maintenance and update of the mesh. With battery estimation technique, a receiver that shows high or low battery capacity for the purpose to become a core or evade to become a core node is detected and removed from the mesh by comparing estimated value and claimed value. Similarly, malicious nodes may alter data or inject spoofed messages in the network. To prevent nodes from tampering with data and generating spoofed messages, a packet authentication process is also used. Packet authentication process uses a digital signature, which assures the integrity of the transmitted packet and any change on digital signature can easily reveal. At the end of paper, Network Simulator-2 is used to observe the performance of protocol and evaluate the conclusion based on results.

Tumor treating fields (TTF) is delivered with an original device causing antimitotic activity in tumor cells of glioblastoma patients. Low intensity and intermediate frequency alternating electric fields are effective over metaphase. Tumor treating fields is a novel modality approved by FDA(Federal Drug Association) for newly diagnosed and also recurrent glioblastoma. Frequency of 200 kHz is given with transducer applied on scalp of patient. Phase 3 trial showed the efficacy of this modality alone equivalent compared to chemotherapy. Skin toxicity is the major side effect of this therapy. TTF is now used in recurrent glioblastoma but future perspective shows that this unique modality can be delivered in different solid tumors.

The (non-volatile) methanol-soluble fraction isolated from DMO-118,a typical asphaltic crude from the La Luna formation, was heated at 200 °C in the presence of the air oxygen (O2) for periods 0.5 to 10 hours. In the first stage heating yields the asphaltenes-like product similar to the DMO-118 asphaltenes. In the later stage heating generates the kerogen-like material similar to the kerogens isolated from the La Luna (QM) source rocks. We used a Fourier Transform Infrared (FTIR) spectroscopy to compare these artificial and natural materials. Kerogenization follows the first order consecutive reaction kinetics with the asphaltenes-like product as an intermediate. Similar oxygenic kerogenization was observed for other asphaltic DMO crudes as well as non-asphaltic DM crudes of the La Luna formation.