Today, many different methods are applied for the correct use of foods and to prevent their deterioration. Ensuring healthy conditions for people in food consumption and consumption of healthy foods is very important for human welfare. In this study, food spoilage, the factors that cause food spoilage, its effects on a global basis, food transport systems (cold chain) and measures that prevent or delay food spoilage are discussed.

When it comes to intelligence and clever behavior, the first picture that comes to mind advanced creatures like mammals and ranked first in humans. However, do non-advanced organisms such as microorganisms have clever intelligence and behavior? Are there strategies and mechanisms for different situations? In addition, do they have the program to survive? Communicative competences and the use of the semiochemical vocabulary enable bacteria to develop, organise and coordinate rich social life with a great variety of behavioral patterns even in which they organise themselves like multicellular organisms. They have existed almost four billion years and still survive,they have been during the evolutionary history and have made adjustments. Recent researches have shown that bacteria are derived ability of the edit genome from viruses. In this paper, microbial intelligence and their presence in microbial communities and illustrates some communication paths between bacteria and viruses were studied. Biotechnological applications of microbial social relationships are determined and the new strategy revealed against pathogenic bacteria based on these same communicative and routine interactions between bacteria.

2Artificial intelligence (AI), described as computer algorithms that exhibit cognitive characteristics like learning abilities, now affecting our lives in many areas. In the medical field, AI-supported image analysis has already taken on a central role in pathology, radiology and dermatology. The policy of this review consisted of peer-reviewed literature annotated in the Web of Science, Scopus, PubMed and Google Scholar databases. Articles were reviewed that describe the use of AI to analyze images to diagnose infectious diseases. Digitization in healthcare is already having a profound impact on patients. It is expected that the development that has started will continue to gain momentum. Machine learning is fundamentally changing the way we interact with health-related data, including clinical microbiology and infectious disease data. We will likely transition from the Internet of Things environment to the Internet of Bodies with devices and providing detailed health data even in disease-free times. The focus of this study was to review current views on attempts to apply AI methods in daily practice, as well as to search for promising methods to diagnose infectious diseases in the most efficient way

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.