Nowadays, we tend to see a busy and fast amount of change in almost every fields of our life. This systematically alters the management domain and forces for business managers to be able to change their strategy at any moment. Therefore change has become quick all round the world and it has changed into a multidimensional idea. During the time of change in which we live, an interactive and fast strategy in political, social, cultural and economic areas to bring up completely different processes. Strategic management constitutes the whole of the efforts to get good results, including the participation of the individual in every organization and the full support of the organizational manager. In this sense, the needs and expectations of stakeholders represent the active role of policy-makers in determining the organization's vision, mission, objectives and performance measurement. The strategy is formulated basically, depending on the vision and mission of an organization. The strategy as a procedure is determined after strategic objectives have been determined. However, determining the purpose depends on the vision again. Therefore, the strategy is formulated in the extension and in parallel with the tendencies of the vision. Strategic management is a management technique that enables the determination of the objectives and targets for the future in all the organizations operating in the private, and public sector. This article is devoted to study the importance of strategic management to business organization. First, it introduces to the targeted topic, then presents a number of strategy’s definitions, after that, the article explains the conceptual analysis of strategic management and its importance and contribution for competing business organization. It also clarifies the link between strategic management and business culture. Finally, the article discusses the consequences that may happen if an organization works without strategic management. Conclusion remarks and a list of references are stated at the end of this article

The world’s most popular business today is online retailing. Online retail market’s aim is to connect buyer and seller from different parts of the world and make product’s available worldwide. They play the role of bridge between buyer and seller. On watching the current internet retailing plans of action we could find that either venture on stock is gigantic or nature of the item is undermined. Along these lines a solidness is required in order to limit the speculation on schedule and venture on distribution center and limit client objections. In our proposed model we advanced these issues, the principal finding is keeping up stock by without a moment to spare strategy for getting the item from purchaser, and the subsequent discovering is quality check of items before arriving at the client. These online retailers are either buyer oriented or seller oriented. Both the business model has it’s own advantage and disadvantage. Inventory model of seller oriented business model is costly but quality check is strong. Buyer oriented business model spends on connecting the two parties rather than maintaining inventory So we developed a model that could stabilize both the business models without compromising on quality of the product being delivered and managing the inventory effectively is proposed in this paper.

Image semantic segmentation is an important research direction in the field of computer vision, and it is also one of the key technologies for intelligent systems to understand natural scenes. Image semantic segmentation technology has broad application prospects in the fields of auto-driving, intelligent monitoring systems, and unmanned aerial vehicle. In recent years, with the development and application of deep learning technology, image semantic segmentation methods based on deep learning have begun to emerge in large numbers. This paper mainly classifies and summarizes the image semantic segmentation techniques based on deep learning. According to the characteristics of the network architecture, the method of image semantic segmentation based on deep learning is divided into FCN-based method, Encoder-Decoder-based method, RNN-based method, and GAN-based method.  We analyze and introduce the representative algorithms of each method, summarize the basic ideas, advantages and disadvantages of each method, systematically explain the contribution of deep learning to image semantic segmentation, and finally predict and analyze possible future development trends and research in this field direction.

This paper describes the design analysis of a biped humanoid robot and their uses in day today life. A human like autonomous robot which is capable to adapt itself with the changing of its environment and continue to reach its goal is considered as Humanoid Robot. These characteristics differs the Android from the other kind of robots. In recent years there has been much progress in the development of Humanoid and still there are a lot of scopes in this field. A number of research groups are interested in this area and trying to design and develop a various platforms of Humanoid based on mechanical and biological concept. Many researchers focus on the designing of lower torso to make the Robot navigating as like as a normal human being do. Designing the lower torso which includes west, hip, knee, ankle and toe, is the more complex and more challenging task. Upper torso design is another complex but interesting task that includes the design of arms and neck. Analysis of walking gait, optimal control of multiple motors or other actuators, controlling the Degree of Freedom (DOF), adaptability control and intelligence are also the challenging tasks to make a Humanoid to behave like a human. Basically research on this field combines a variety of disciplines which make it more thought-provoking area in Mechatronics Engineering.

The culture of a community is the real determinant of the behavior in that community and on its lifestyle, it is the source of the ideas, customs, values, traditions and arts. The impact of culture goes into the daily details of every individuals in the community and directly affects their behavior, and the way they act or think. Therefore; the cultural background of every customer or client plays major role in evaluating the service provided to him. Therefore; to know and understand the role played by different cultures in evaluating the level of the service is very substantial for every organization or supplier.

As by means of understanding the customer's culture the organization can provide services most favored and best liked by customers. This research aims at studying the impact of the tourists' cultural background on his satisfaction towards the offered services by relying on Hofstede's Cultural Dimension :

1. Power distance.
2. Uncertainty avoidance.
3. Individualism vs. collectivism.
4. Masculinity vs. femininity.
5. Long- vs. short-terms orientation.

We tried, in this study, to understand the role the cultural background plays in determining the level of customers' satisfaction towards the offered touristic services, then, examining its impact  on their behavioral intentions about the services in North Africa countries (Libya, Tunisia, Algeria, Morocco, and Mauritania).

The study concluded with a set of results that stressed the presence of a great difference in evaluation made by customers who have different backgrounds about the touristic services, which emphasizing the significance of the cultural dimensions in evaluating the services. Therefore; the study suggested some recommendations, the most significant of them is the employees engage in providing such services should grasp well the cultural issues of their customers, in addition to the importance of focusing on the training of the employees in tourism sector on how to deal with customers of different cultures.

In recent years, deep learning has achieved good results, and each born technology has been tried in different fields. The generative adversarial network has received extensive research and high attention from the artificial intelligence academia and industry since its introduction. With the development of deep learning technology, the generative adversarial model has been continuously promoted in theory and application. Generative Adversarial Network (GAN) has become a research hotspot in the field of artificial intelligence. It brings new vitality to the research of unsupervised learning and is of great significance to the development of generative models. Aiming at the hot model of generative adversarial network, the basic principles of generative adversarial network and several typical variations improved on it are introduced. At present, generative adversarial networks have been successfully applied in the fields of computer vision, speech, and natural language processing.

In image processing, noise in the image has become an important factor affecting image quality. The appearance of noise will cause important information in the image to be lost, and blurring will occur, affecting the image quality.

The neighborhoods used in traditional spatial domain image processing algorithms are mostly squares, but the neighborhoods of pixel points in real images have various shapes. Traditional algorithms cannot adapt to the characteristics of neighborhood diversity. This paper first proposes an image denoising algorithm based on neighborhood clustering. The algorithm focuses on generating a dynamic neighborhood, then integrating the pixel information in the neighborhood and completing the denoising. This algorithm has achieved good denoising effect. Compared with the traditional spatial domain denoising algorithm, the peak noise ratio is significantly improved.