Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

     In recent years, landslides in mountainous provinces have been increasing. Whenever the rainy season comes, landslides occur, causing casualties to people and the infrastructure. Bac Can, which has suffered significant damage from landslides, is a mountainous province in the north of Vietnam. This study introduces the applying the analytic hierarchy process (AHP) to study landslides in Bac Can province, Vietnam. Currently, the earth's temperature is rising, and natural and anthropogenic hazards in the mountain are causing hazards. Integrate elements of the DEM model with component maps and different precipitation scenarios as the basis for the author to issue landslide warnings under the climate change scenario with maximum day

Furfural is one of the one of pollutants in refinery industrial wastewaters. In this study advanced oxidation process using UV/H₂O₂ was investigated for furfural degradation in synthetic wastewater. The results from the experimental work showed that the degradation of furfural decreases as its concentration increases, reaching 100% at 50mg/l furfural concentration and increasing the concentration of H₂O₂ from 250 to 500 mg/l increased furfural removal from 40 to 60%.The degradation of furfural reached 100% after 90 min exposure time using two UV lamps, where it reached 60% using one lamp after 240 min exposure time. The rate of furfural degradation k increased at the pH and initial concentratio

Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

Process capability provides a quantitative measure for gasoline production conformance to specifications.It was measured throughout four consecutive months of the last quarter of 2011. Results revealed high percentages (up to 44%) of non conforming gasoline blends to Iraqi marketing specifications for petroleum products (2000) by inspecting 122 different samples of Iraqi regular gasoline (RON 85).
Quality cost analysis as an important financial control tool was carried out to evaluate Cost of Quality (COQ) which was large due to non conforming gasoline reached up to (722.8 M.ID) in October. In this research COQ was investigated in order to identify the opportunities of gasoline quality improvements through production process. Also cus

In this paper a nonlinear adaptive control method is presented for a pH process, which is difficult to control due to the nonlinear and uncertainties. A theoretical and experimental investigation was conducted of the dynamic behavior of neutralization process in a continuous stirred tank reactor (CSTR). The process control was implemented using different control strategies, velocity form of PI control and nonlinear adaptive control. Through simulation studies it has been shown that the estimated parameters are in good agreement with the actual values and that the proposed adaptive controller has excellent tracking and regulation performance.