Compared to a photovoltaic array, a photovoltaic/thermal concentrator module can produce thermal power for various productions in downstream cycles in addition to electrical energy. In this study, the system for the combined production of electricity, heat and cooling based on a photovoltaic/thermal concentrator has been evaluated. In this triple production system, a lithium bromide-water absorption chiller with a cooling capacity of 5 kW was used. In the organic Rankine power generation cycle, the annual exergy rate of the incoming stream was almost 48 MWh, the annual production exergy rate was about 54.4 MWh and the annual exergy destruction rate was ~43.1 MWh. According to the results, the freshwater production rate of the desalination plant was approximately 56.7 m³/year; the lowest month was 3.8 m³ in November.

The Prediction and detection disease in human lungs are a very critical operation. It depends on an efficient view of the CT images to the doctors. It depends on an efficient view of the CT images to the doctors. The clear view of the images to clearly identify the disease depends on the segmentation that may save people lives. Therefore, an accurate lung segmentation system from CT image based on proposed CNN architecture is proposed. The system used weighted Softmax function the improved the segmentation accuracy. By experiments, the system achieved a high segmentation accuracy 98.9% using LIDC-IDRI CT lung images database.

This paper proposed a new architecture for handwriting word recognition system Based on Support Vector Machine SVM Classifier. The proposed work depends on the handwriting word level, and it does not need for character segmentation stage. An Arabic handwriting dataset AHDB, dataset used for train and test the proposed system. Besides, the system achieved the best recognition accuracy 96.317% based on several feature extraction methods and SVM classifier. Experimental results show that the polynomial kernel of SVM is convergent and more accurate for recognition than other SVM kernels.

Biometrics is a field that uses behavioral and biological traits to identify/verify a person. Characteristics include handwrittien signature, iris, gait, and fingerprint. Signature-based biometric systems are common due to their simple collection and non-intrusive. Identify the humans using their handwritten signatures has received an important attention in several modern crucial applications such as in automatic bank check, law-enforcements, and historical documents processing. Therefore, in this paper an accurate handwritten signatures system is proposed. The system uses a proposed preprocessing stage for the input handwritten signatures images. Besides, a new deep learning model called MobileNets, which used for classification process. Support vector machine (SVM) used as a classifier with the MobileNets inorder to get a better identifaction results. Experimental results conducted on standard CEDAR, ICDER, sigcomp handwritten signature datasets report 99.8%, 98.2%, 99.5%, identification accuracy, respectively.

With the rapid development of technology in all life fields, and due to the huge daily needs for banking systems process, documents processing and other similar systems. The authentication became more required key for these systems. One of the successful system to verify the any person is the signature verification system. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. Therefore, this paper present a reliable signature verification system using proposed histogram of sparse codes (HSC) feature extraction approach and artificial neural networks (ANN) architecture for classification. The system achieved fast computing 0.09 ms and accurate verification results that is 99.7% using three different signature images datasets CEDAR, UTSig, and ICDAR.