The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating opti

The Internet of Things (IoT) is an information network that connects gadgets and sensors to allow new autonomous tasks. The Industrial Internet of Things (IIoT) refers to the integration of IoT with industrial applications. Some vital infrastructures, such as water delivery networks, use IIoT. The scattered topology of IIoT and resource limits of edge computing provide new difficulties to traditional data storage, transport, and security protection with the rapid expansion of the IIoT. In this paper, a recovery mechanism to recover the edge network failure is proposed by considering repair cost and computational demands. The NP-hard problem was divided into interdependent major and minor problems that could be solved in polynomial t

It is a well-known fact that publishing companies spend much money, time and energy in designing their book covers to attract potential customers. As the first thing people do when they buy or intend to buy a book is looking at its front cover. However, if there is a need to know more about the book, people usually look at the information on its back cover. This paper attempts to explore the persuasive function of blurbs beyond the constraints of the academic domain and consequently their connection with advertising discourse in two main sections: The first presents the concept of blurb and its structure while the second defines persuasion and shows the most prominent strategiesused in blurbs. Finally, this paper gives the conclusion tha

This work involves the calculation of the cooling load in Iraqi building constructions taking in account the effect of the convective heat transfer inside the buildings. ASHRAE assumptions are compared with the Fisher and Pedersen model of estimation of internal convective heat transfer coefficient when the high rate of ventilation from ceiling inlet configuration is used. Theoretical calculation of cooling load using the Radiant Time Series Method (RTSM) is implemented on the actual tested spaces. Also the theoretical calculated cooling loads are experimentally compared by measuring the cooling load in these tested spaces. The comparison appears that using the modified Fisher and Pedersen model when large ventilation ra