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. In this paper, an inclusive study has been prepared that aims to minimize energy waste of domestic purpose in Iraq that is considered an important aspect which is almost neglected at the present time as energy sources tend to be exhausted as excessive use causes other problems such as environmental pollution and global warming, and it shows the solutions as duties of each people and the government to be performed regularly in order to achieve the goal of the study. It also addresses some obstacles and how to overcome them through the cooperation of all concerned collections for developing strategic plans and energy management programs that can be implemented and monitored through the committees of specialized experts. The results of energy rationalizing show that the reduction of the domestic electrical consumption in the summer season in Iraq is about 52% of total power and 25% in a winter

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The Rheumatoid Arthritis (RA) affects many people targeting their joints, especially small joints, and it targets all ages which it is more common in women. This disease has many symptoms similar to other diseases. Therefore, it is very hard to detect. Also, the diagnostic tools are complex and uneconomical. In this paper, artificial intelligence network used for diagnosis and early detection of RA in accordance with criteria developed by the American College of Rheumatology. The best performance occurs with the minimum number of neurons required when the number of neurons is 6. So that, the performance is equal to 3.8968x1010-. When reducing the number of neurons to 5 or increasing to 8, the result is 0.0041 and 1.0611×10-10, respectively. However, all results can be considered acceptable and indicate that the best choice from this structure will be 6 neurons in the form of complexity and accuracy.

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In this paper, the performance of the Multiple Input Multiple Output (MIMO) technique evaluated in term of Bit Error Rate (BER) with respect to Signal to Noise Ratio (SNR) by using Binary Phase Shift Keying (BPSK) modulation for two algorithms Maximum Likelihood (MLD) and Zero-Forcing (ZF) with different configurations of antennas array in Rayleigh and Additive White Gaussian Noise (AWGN) channels. The results were compared between them to determine which of the numbers antenna elements are suitable in the transmitter and receiver of each algorithm. The results of MLD offers a better configuration when 4×4 and 3×4 antennas array were used, while the ZF remains the same performance for the 2×2, 3×3 and 4×4 configurations. In different numbers of antennas, the best performance of ZF is got when the number of transmitter and receiver antennas are equal to 2×4 respectively. Also, the last one is better than the 4×4 and 3×4 configurations of MLD algorithm.

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The Inter-Symbol Interference (ISI) is a challenging problem in a wireless channel that degraded communication links. One of the efficient methods to reduce the ISI is the adaptive equalizer. In the wireless channel, the ISI elimination required an algorithm with powerful computation due to the fast changing of the wireless channel properties. In this paper, a novel Selective Mapping Variable Step Size Normalized Least Mean Square (SMVSS-NLMS) algorithm has been introduced to mitigate the ISI efficiently. The SMVSS-NLMS algorithm operates by computing a mapping of weight vectors from many variable step sizes and selects the vector that gives the minimum error. The SMVSS-NLMS algorithm has been tested and compared to the Tuning-Free VSS-NLMS algorithm (TFVSS-NLMS). The simulation results have been observed under complex static and dynamic wireless channels using Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift (QPSK) modulation schemes. The Bit Error Rate (BER) performance of SMVSS-NLMS considerably outperformed the TFVSS-NLMS at typical levels of signal to-noise ratio (SNR). Moreover, the SMVSS-NLMS offered faster convergence speeds (12 iterations) and lower Mean Square Error (MSE) (-79 dB) than the TFVSS-NLMS (88 iterations, -14 dB MSE).

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A novel Variable Step Size Proportional algorithm (VSSP) has used for the sun-tracking system. The proposed algorithm does not require any feedback, and it can track the sun accurately even when there are obstacles around. Image processing techniques have been used to estimate the location of the sun, hence controlling the motors in each axis. A microcontroller board has been used to exchange data between the Matlab software, where the algorithm has written, and the peripherals. The system has offered an excellent ability to track the center of the sun in 15 iterations with 97% accuracy. The accuracy has been improved even more after 24 iterations to become 98.7%. Tracking the intensity of light is the most powerful prosperity of this algorithm that makes it able to track the sun even if it is out of the camera range. It does not require any complex processing or special sensors. The proportional controller is the base of this algorithm.

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In the optical channel, each of pulse broadening and distortion produced by dispersion, lead to degrading the fiber performance. To deal with the dispersion, several techniques have developed and improved. To mitigate the pulse broadening in the optical channel, Fiber Bragg Gratings (FBGs) and Dispersion Compen-sation Fiber (DCF) have utilized separately. Single channel transmission system with 10 Gbits/sec data rate optical system is designed and simulated by using OptiSystem software package. All the designs are com-pared in forms of Quality factor (Q-factor). The optimum parameters setting of FBG are achieved by vary-ing the chirp function and Apodization function of the FBGs. Among the methods presented in this work, the Q-factor of DCF is better than using FBGs, for 100 km length, The Q-factor by using DCF is 10.08 and by using FBG is 4.83, but DCF needs huge negative dispersion coefficient, and it has high insertion loss with in-creasing the nonlinear effects and costly, so that FBG is a promising approach in compensating the dispersion.

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Diabetes is a life-threatening syndrome occurring around the world; it can have huge complications and is documented by large amounts of medical data. Therefore, attempts at early detection of this disease took a large area of research and many methods were used to deal with diabetes. In this paper, different types of KNN algorithm have been used to classify diabetes disease using Matlab. The dataset was generated by the criteria of the American diabetes association. For the training stage, 4900 samples have been used by the classifier learner tool to observe the results. Then, 100 of the data samples were used for the test. The results show that the KNN types (Fine, Weighted, Medium and Cubic) give high accuracy over the Coarse and the Cosine methods. Fine KNN is considered the most suitable according to its accuracy of classified samples.