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Artificial intelligence has been widely used in various applications such as health and safety, smart homes, greenhouses, and industrial application. It has been increasingly utilized in the industry owing to its benefits in terms of enhancing the overall performance of a given system. This study appeared from a real need in many local industries. In this paper, a prototype system has been implemented for artificial control on the temperature of the industrial panel. The paper includes two control systems executed; classical PID (Proportional Integral Derivative) and fuzzy logic with a comparison between them. Fuzzy control algorithm is developing based on Sugeno method inside PLC (Programmable Logic Controller). The connection of PLC with sensors is used by the Modbus protocol. Arduino UNO and Ethernet shield are used to connect the sensor to the router and then to PLC by Modbus.

In the past two decades, the Internet has become as open, publicly and widely used as a source of data transmission and exchanging the messages between criminals, terrorists and those who have illegal motivations. Moreover, exchanging important data between various military and financial institutions, even ordinary citizens. From this view, there is one of the important means of exchanging information widely used on the Internet medium is e-mail. Email messages are digital evidence that has been become one of the important means to adopt by courts in many countries and societies as evidence relied upon in condemnation. This paper presents a distinct technique for classifying emails based on data processing and mining, trimming, refinement, and then adapts several algorithms to classify these emails and then using SWARM algorithm to obtain practical and accurate results also using hybrid English lexical dictionary SentiWordNet3.0 for email forensic analysis then deal with a machine learning algorithm. The proposed system is capable of learning in an environment with large and variable data. To test the proposed system, have to select available data which Enron Data set. A high accuracy rate (95%) was obtained, which is higher than the classification rates mentioned in previous research papers presented in section 2 in this paper.

The number of cancer diagnoses and deaths worldwide is rising every year despite technological advancements in diagnosing and treating multiple forms of cancer. An oncolytic virus is a type of tumor-killing virus that can infect and analyze cancer cells while mostly preserving normal cells. The oncolytic Vesicular-Stomatitis Virus therapeutic's cell cycle-specific action mathematically investigated. An optimal Proportion Integral-Derivative (PI-D) controller is introduced in this paper based on a suggested Improved Crow Search Algorithm (ICSA) to enhance the outcome of oncolytic virotherapy. The control technique was tested in a computer using MATLAB simulation. The suggested ICSA is used to tune the parameters of the PI-D controller. The ICSA used the inertia factor and boundary handle mechanism in the position update equation to balance exploration and exploitation. The simulation results show that decrease in total dose, tumor cells to 30%, the tumor remain in the treatment area from day 30 onwards. Furthermore, the ICSA algorithm outperforms the CSA and PSO algorithms by 34.5497×10-6 and 15.2573 ×10-6, respectively, indicating the robustness of treatment methods that can accomplish tumor reduction through biological parameters ambiguity.

The main drawback of the Orthogonal Frequency Division Multiplexing (OFDM) with Differential Chaos Shift Keying (DCSK) that is named (OFDM-DCSK) is the high Peak to Average Power Ratio (PAPR). In this paper, clipping and companding techniques are suggested to overcome the PAPR problem in the OFDM-DCSK system. For the clipping technique, the clipping function is applied before transmitting the signal without the need for an inverse function at the receiver side. While for companding techniques, the commanding function is applied at the end of the transmitter side and the corresponding decompanding function is applied at the receiver to recover the original signal. Different companding techniques are investigated including Hyperbolic, A-Law, and Mu-Law companding function that are compared with the Palm clipping technique. The MATLAB simulation result shows that the Mu-Law technique has the best PAPR reduction (7.22 dB) with a good bit error rate (BER) performance when the number of subcarriers is equal to 512.

Network safety has become an important issue in recent years. Encryption has been developed as a solution and plays an important role in the security of information systems. Many methods are required to secure the shared data. The advanced internet, networking firms, health information and the cloud applications have significantly increased our data every minute. The current work focuses on cryptography to provide the protection for sensitive data that exchanged between personal users, companies, organizations, or in the cloud applications and others during the transfer of data across the network. Firstly, Data sent from sender to network receiver must be encrypted using the cryptographic algorithm. Secondly, the recipient shows the original data using the decryption technique. This paper presents three encrypting algorithms such as AES, RSA and hybrid algorithms, and their efficiency is compared based on the analysis of the time. Results of the experiments show that the hybrid algorithm is better in term of security.

This paper explores the sizing optimization of stand -alone hybrid energy system (HES) in southern Iraq (Thi Qar province) for supply stand-alone households by the electricity. HES consist of three components (solar cell (PV), diesel generator (DG) and battery storage (BS)). Particle swarm optimization (PSO) used in this study for find optimal sizing of the HES to minimizing multi-objective, first objective is to minimizing the total system cost (TSC) that lead to minimizing cost of energy (COE). Second objective is to minimizing total emission CO2 (TECO2). The constraint of the optimization is the reliability (100 %) mean continuous provide the load demand by the electricity. The results of the optimization show the ability the algorithm to minimizing the multi-objective with continuous supply the load by the electricity through life cycle of the project (25) years.

Due to the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

This paper introduces chaotic motion control of autonomous differential drive wheeled mobile robot to cover a certain surrounded area completely and as fast as possible. The suggested method was based on utilization of chaotic logistic map to accomplish a complete coverage of entire area. The trajectory is created by a microcontroller and fed to the designed robot using the proposed chaotic trajectory generator. The designed robot was occupied with infrared sensors to avoid the unforeseen obstacles during the navigation. The mathematical model of the designed robot and the motion control strategy were described in details. Computer simulation tests as well as the practical tests by using the designed robot show satisfactory results and confirm the success of the proposed method.

The aim of this paper is to reach the best balance point between the maximum reliability and minimum lost cost when injecting Distributed Generation (DG). The new Al-Kadimeya substation in Al-Kadimeya sector in Iraq – Baghdad was chosen for this research, because there is a group of four diesel generator injected in it is mesh. These DGs suffers from stop working and interruptions in the supply of energy, which causes its failure to do its task of supporting the national grid in electrical power. This substation has fifteen different feeders. Five diverse feeders were selected to inject DGs in it. The injection was in several locations on the selected five feeders sequentially. Zone branch methodology applied in analysis these feeders and the required calculations were carried out until the best site with the lowest failure rates was reached to inject these generators, which is feeder 2. The failure rates of the five feeders were reduced by 151 times, about 14%. Outages duration were reduced by five h/y, approximately 3.67%.The lost power costs for all previous test sites of injecting distributed generation were then calculated too. Then the site with the highest lost power (i.e. the one of the highest lost cost) was selected to inject the generators to it which was feeder 2 also, where the expected lost costs due to interruptions were reduced by 510,000 USD /y, about 16.4%. Results show that feeder 2 represents the best location for injection of the DGs instead of the current location on feeder 4.

This paper presents an analysis to multipath fading channel effects on the performance of multiple frequency shift keying (MFSK) modulation that used in mobile wireless communications. These effects take account of AWGN, Rayleigh, and Rician multipath fading environments. The analysis also includes the block coding (BC) in error control coding is used in MFSK receivers such as interleaving, Reed Solomon BC, and Alamouti space-time block coding (STBC). The performance analysis of these models is based on bit-error rate (BER) of uncoded and coded MFSK systems when such models operate under multipath fading channels. The simulation results show that an additional enhancement in BER performance and coding gain is gained by adding a space-time block coding to the combination of interleaving and RS coding compared with uncoded MFSK model but at expense of system complexity

This paper presents an efficient static state estimator that is suitable for real time monitoring of the power system. The proposed algorithm includes a weighted least square (WLS) method based on rectangular coordinates system. A predictor-corrector iterative technique is used in solving the state estimator model which is described by a set of nonlinear equations relating measured quantities and the state variables. The proposed algorithm is enhanced by selecting the allocation the PMUs meters optimally by using Particle Swarm Optimization (PSO). The proposed algorithm is applied to the IEEE14 bus and IEEE-30 bus test systems. The obtained results reveal the significant contribution of the proposed algorithm in optimal estimate of the static state in terms of number of iterations for convergence, execution time and accuracy.

Low Density Parity-Check (LDPC) codes are one of the hottest topics in coding theory today. This code invented in the early 1960's, and it having an amazing comeback in the last few years. In this paper, a scheme is proposed to analyze the performance of LDPC which is considered important rival to turbo codes, Convolutional Code (CC) and Read Solomon (RS) in terms of performance and complexity. This code is utilized in Orthogonal Frequency Division Multiplexing (OFDM) system to evaluate the improvement in the Bit Error Rate (BER) performance of the system. Comparison is done between turbo code and LDPC code through Additive White Gaussian Noise (AWGN) and fading channels. The effect of varying the code rate channel, number of iteration and the code length on the performance of the scheme are also explained.

Extensive use of digital photographic devices has resulted in large volumes of digital images being acquired and stored in databases. Whether it is for scientific research, medical or social networking, there is a growing demand for effective retrieval of digital images based on their visual content (e.g.colour and texture). Content-Based Image Retrieval systems are developed to meet this demand. However, searching for similar and relevant images from large-scale databases still poses a challenge for Content-Based Image Retrieval systems due to the gap between high-level meaning and low-level visual features. This paper reviews different Content-Based Image Retrieval approaches such as Clustering, Region-of-Interest, Bag-of-Visual-Words, Relevance Feedback, Browsing, and indexing that have been developed to reduce such “Semantic gap” issue. So, the interested researchers can interest to determine which method is benefit to his work.