One of the most interested problems that recently attracts many research investigations in Protein-protein interactions (PPI) networks is complex detection problem. Detecting natural divisions in such complex networks is proved to be extremely NP-hard problem wherein, recently, the field of Evolutionary Algorithms (EAs) reveals positive results. The contribution of this work is to introduce a heuristic operator, called protein-complex attraction and repulsion, which is especially tailored for the complex detection problem and to enable the EA to improve its detection ability. The proposed heuristic operator is designed to fine-grain the structure of a complex by dividing it into two more complexes, each being distinguished with a core protein. Then, it is possible for each of the remaining proteins associated with the original coarse-grained complex to repulse from one of the new generated complexes while attracted by the core protein of the second complex. The topology-based complex detection models presented in the literature are adopted to inter-play with the proposed heuristic operator inside the EA general framework. To assess the performance of the EA when coupled with the proposed heuristic operator, the well known Saccaromycaes Cerevisiae yeast PPI network and one reference set of benchmark complexes created from MIPS are used in the experiments. The results prove the positive impact of the heuristic operator to harness the strength of almost all adopted EA models.
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تتحقق اهداف الدول عبر توظيف امكانياتها ومواردها ، وهذا التوظيف يقترن بوسائل مختلفة باختلاف الامكانيات المتاحة. وتتفاوت هذه الوسائل ما بين الاكراه والترغيب ، واحياناً من الممكن استخدام كلا الوسيلتين ، وتندرج هذه الوسائل من حيث تصنيفها ضمن نوعين رئيسين هما: القوة الصلبة ]القوة العسكرية والاقتصادية[ والقوة الناعمة ]استخدام جميع ادوات الترغيب وتسخيرها من اجل ان تُعجب بها الدول الاخرى وتنصاع
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In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne
... Show More3D models delivered from digital photogrammetric techniques have massively increased and developed to meet the requirements of many applications. The reliability of these models is basically dependent on the data processing cycle and the adopted tool solution in addition to data quality. Agisoft PhotoScan is a professional image-based 3D modelling software, which seeks to create orderly, precise n 3D content from fixed images. It works with arbitrary images those qualified in both controlled and uncontrolled conditions. Following the recommendations of many users all around the globe, Agisoft PhotoScan, has become an important source to generate precise 3D data for different applications. How reliable is this data for accurate 3D mo
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Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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Improvisation is that spontaneous automatic achievement which is formed by a new cognition that is not based on something prior to it or a previous cognition. It is instantaneous. Whereas in art, improvisation also inters in all types of applied and performance arts as a foundation for launching and initiating it in music, painting, cinema, television and theater. In order to study the improvisation of the actor, the researcher put forward a theoretical study that included two sections. The first section is (the improvisation concept) and the second section is (improvisation in the show). The researcher, in the research procedures, took an intentional sample that was represented by the theatrical show (Rehearsal in Hell Play) and after t
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Non-orthogonal Multiple Access (NOMA) is a multiple-access technique allowing multiusers to share the same communication resources, increasing spectral efficiency and throughput. NOMA has been shown to provide significant performance gains over orthogonal multiple access (OMA) regarding spectral efficiency and throughput. In this paper, two scenarios of NOMA are analyzed and simulated, involving two users and multiple users (four users) to evaluate NOMA's performance. The simulated results indicate that the achievable sum rate for the two users’ scenarios is 16.7 (bps/Hz), while for the multi-users scenario is 20.69 (bps/Hz) at transmitted power of 25 dBm. The BER for two users’ scenarios is 0.004202 and 0.001564 for
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This study was conducted in Animal Resources Department , College of Agriculture to estimate the effect of chemical and biological treatments to improve the nutritive value of poor quality roughages ( corn cobs and wild reed ) . The feeds were treated chemically with 4% NaoH solution ,whereas Aspergillus niger was used to ferment corn cobs and wild reed samples . The chemical analysis showed that protein percentages of corn cobs and wild reed was increased significantly (P<0.05) from 6.05% to 10.51% and 17.70% and from 3.10 %to 6.50% and 9.96% for both chemical and biological treatments respectively. The crude fiber percentages decreased significantly (P<0.05) from 29.19% and 26.10% to 23.60% and 20.10% for chemical treatment and was 20
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Background and Aim: due to the rapid growth of data communication and multimedia system applications, security becomes a critical issue in the communication and storage of images. This study aims to improve encryption and decryption for various types of images by decreasing time consumption and strengthening security. Methodology: An algorithm is proposed for encrypting images based on the Carlisle Adams and Stafford Tavares CAST block cipher algorithm with 3D and 2D logistic maps. A chaotic function that increases the randomness in the encrypted data and images, thereby breaking the relation sequence through the encryption procedure, is introduced. The time is decreased by using three secure and private S-Boxes rather than using si
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In modern times face recognition is one of the vital sides for computer vision. This is due to many reasons involving availability and accessibility of technologies and commercial applications. Face recognition in a brief statement is robotically recognizing a person from an image or video frame. In this paper, an efficient face recognition algorithm is proposed based on the benefit of wavelet decomposition to extract the most important and distractive features for the face and Eigen face method to classify faces according to the minimum distance with feature vectors. Faces94 data base is used to test the method. An excellent recognition with minimum computation time is obtained with accuracy reaches to 100% and recognition time decrease
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