Sentiment analysis is an automatic way to determine that whether opinions of people about a subject are favorable or unfavorable. One of the most important sub tasks in sentiment analysis is to determine the sequence of words affected by negation. Most of the existing sentiment analysis systems used traditional methods based on static window and punctuation marks to determine the scope of negation. However, these methods do not offer satisfactory performance due to variability in the negation scope, inability to deal with linguistic features and improper word sense disambiguation. In this paper, we investigate the problem of identifying the scope of negation while determining the polarity of a sentence. We propose a negation handling method based on linguistic features which determine the effect of different types of negation. Experiment results show that the proposed method improves the accuracy of both negation scope identification and overall sentiment analysis.

a b s t r a c tOpinion mining mainly involves three elements: feature and feature-of relations, opinion expressions and the related opinion attributes (e.g. Polarity), and feature-opinion relations. Although many works have emerged to achieve its aim of gaining information, the previous researches typically handled each of the three elements in isolation, which cannot give sufficient information extraction results; hence, the complexity and the running time of information extraction is increased. In this paper, we propose an opinion mining extraction algorithm to jointly discover the main opinion mining elements. Specifically, the algorithm automatically builds kernels to combine closely related words into new terms from word level to phrase level based on dependency relations; and we ensure the accuracy of opinion expressions and polarity based on: fuzzy measurements, opinion degree intensifiers, and opinion patterns. The 3458 analyzed reviews show that the proposed algorithm can effectively identify the main elements simultaneously and outperform the baseline methods. The proposed algorithm is used to analyze the features among heterogeneous products in the same category. The feature-by-feature comparison can help to select the weaker features and recommend the correct specifications from the beginning life of a product. From this comparison, some interesting observations are revealed. For example, the negative polarity of video dimension is higher than the product usability dimension for a product. Yet, enhancing the dimension of product usability can more effectively improve the product.

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Supply chain management encompasses various processes including various conventional logistics activities, and various other processes These processes are supported -to a certain limit -by coordination and integration mechanisms which are long-term strategies that give competitive advantage through overall supply chain efficiency. Information Technology, by the way of collecting, sharing and gathering data, exchanging information, optimising process through package software, is becoming one of the key developments and success of these collaboration strategies. This paper proposes a study to identify the methods used for collaborative works in the supply chain and focuses on some of its areas, as between a company and its suppliers (i.e., inventory sharing) and its customers (i.e., customer demand, forecasting), and also the integration of product information in the value chain.

This article presents comparison between data rate or rate control, that is, video transmission rate control algorithm and transmission power control algorithms for two different cases. First, energy consumption due to high peak variable data rates in video transmission. Second, energy depletion due to high transmission power consumption and dynamic nature of wireless on-body channel. The former one focuses on constant (fixed) transmission power level and variable data rate (''severe'' conditions), for example, medical monitoring of the emergency patients. The latter considers variable transmission power level and constant (fixed) data rate (''less severe'' conditions), for example, electrocardiography measurement for patients in wireless body sensor networks. Besides, energy efficiency comparison analysis of battery-driven or video transmission rate control algorithm and transmission power control-driven or power control algorithm is presented. Finally, proposed algorithms are analyzed and categorized as energy-efficient and battery-friendly for medical applications in wireless body sensor networks.

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This work aims to review literature related to the latest cyber-physical systems (CPS) for manufacturing in the revolutionary Industry 4.0 for a comprehensive understanding of the challenges, approaches, and used techniques in this domain. Different published studies on CPS for manufacturing in Industry 4.0 paradigms through 2010 to 2019 were searched and summarized. We, then, analyzed the studies at a different granularity level inspecting the title, abstract, and full text to include in the prospective study list. Out of 626 primarily extracted relevant articles, we scrutinized 78 articles as the prospective studies on CPS for manufacturing in Industry 4.0. First, we analyzed the articles’ context to identify the major components along with their associated fine-grained constituents of Industry 4.0. Then, we reviewed different studies through a number of synthesized matrices to narrate the challenges, approaches, and used techniques as the key-enablers of the CPS for manufacturing in Industry 4.0. Although the key technologies of Industry 4.0 are the CPS, Internet of Things (IoT), and Internet of Services (IoS), the human component (HC), cyber component (CC), physical component (PC), and their HC-CC, CC-PC, and HC-PC interfaces need to be standardized to achieve the success of Industry 4.0.