Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

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<span>In this paper, an android expert system for recommending the suitable journal for publishing the researchers' papers has been presented. In so doing, the expectations of different journals for accepting an article and also the requests of papers' writers for choosing the journals have been examined. Language, quality, waiting time for judgment, waiting time for publication after acceptance, field, length of paper and cost are the system inputs and its output is the degree of suitability of journals for publishing a certain paper. This system includes a database of different journals and their parameters. It uses particle swarm optimization method and K-means++ algorithm for assessing and clustering the journals database and determines an index for every cluster of journals. The process for matching the paper with a cluster's index is done through fuzzy induction system. After choosing the most similar cluster, the paper is compared with all the journals of the cluster in the same way and the result including the most similar journals is presented. This system has been tested for journals and papers in the computer field indexed in Elsevier.</span>

Wireless Sensor Networks are one of the most important distributed networks which are used in the wide range of applications. Energy is one of the major limitations of wireless sensor networks, which has direct impact on the network's life time. LEACH protocol is one of the most wellknown Clustering schemes that select cluster heads randomly. Clustering is an effective topology control Approach in wireless sensor networks. In this paper, we proposes new clustering algorithm that using an imperialist competitive algorithm to select cluster heads in LEACH algorithm. Simulation results show proposed algorithm can prolong the network lifetime efficiently compared with LEACH protocol. General TermsMeta heuristic algorithms, clustering.

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