Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both so

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The prevalence of using the applications for the internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices

The process for preparing activated carbon (AC) made from tea residue was described in this paper. Investigated were the physicochemical characteristics and adsorption efficiency of the produced AC. Activation with potassium hydroxide (KOH) and carbonization at 350 °C are the two key steps in the manufacturing of AC. The activated carbon was used to adsorb Tetracycline (TC). Different parameters were studied at room temperature to show their effects on the adsorption efficiency of TC. These parameters are the initial concentration of adsorbate TC, solution acidity pH, time of adsorption, and adsorbent dosage. The prepared active carbon was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microsc

The process for preparing activated carbon (AC) made from tea residue was described in this paper. Investigated were the physicochemical characteristics and adsorption efficiency of the produced AC. Activation with potassium hydroxide (KOH) and carbonization at 350 °C are the two key steps in the manufacturing of AC. The activated carbon was used to adsorb Tetracycline (TC). Different parameters were studied at room temperature to show their effects on the adsorption efficiency of TC. These parameters are the initial concentration of adsorbate TC, solution acidity pH, time of adsorption, and adsorbent dosage. The prepared active carbon was characterized using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (

Occupy public investment spending, a great deal of attention since the thirties of the last century, to play important role in economic and social development process and therefore most of the countries are trying different degree of economic development to the completion of the largest amount of public investment, especially in infrastructure, which is one of the pillars essential to economic development, and in order to maximize utilization of the completion of public investment should focus on achieving efficiency in completion.
    The current reality of public projects in Iraq, indicating the absence of several key characteristics that must be provided if these proje