The purpose of this paper is to systematically analyze the knowledge management research within small and medium-sized companies. The study includes a systematic review of 30 peer reviewed papers on knowledge manageme...
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The purpose of this paper is to systematically analyze the knowledge management research within small and medium-sized companies. The study includes a systematic review of 30 peer reviewed papers on knowledge management advantages for SMEs. Balanced scorecard perspectives cover all aspects of the organization, and, consequently, the balanced scorecard approach has been applied to classify the KM benefits. The reviewed scientific studies highlight the benefits of knowledge management in the areas of economic and social perspective (increased profits, flexibility, product reputation, financial performance), commercial and customers perspective (market share, sales growth, customer satisfaction, good external relationship), internal business processes perspective (operational performance, increased productivity, product/service quality, process improvement) and organizational learning and growth perspective (employee development, innovation, organizational creativity, learning).For future studies, determining stakeholder views is recommended in order to gain sustainable competitive advantage.
The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-bas...
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The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.
We present a unique Geographical information System (GIS) that seamlessly integrates 2D and 3D views of the same spatial and aspatial data. Multiple layers of information are continuously transformed between the 2D an...
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The exact method JOCOR, proposed by Mueen et al., is the first method for joining two time series on subsequence correlation. Although JOCOR requires the time complexity O(n2lgn), where n is the length of the time ser...
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A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. There have been a number of research works that focused on how to extract rules from trained n...
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ISBN:
(纸本)9781450329309
A significant limitation of neural networks is that the representations they learn are usually incomprehensible to humans. There have been a number of research works that focused on how to extract rules from trained neural networks. Recently, Kamruzzaman et al. have developed an efficient algorithm, called REANN, for extracting rules from trained neural networks for classification problem. Given a trained network, REANN produces a set of rules that approximates the function represented by the network. In this paper, we investigate a case study in which we apply REANN to neural networks used in a more complex context: time series prediction. We devise some modifications to REANN to adapt this algorithm to the problem of time series prediction. Experimental results on three real world time series datasets demonstrate the effectiveness of the proposed approach in generating accurate rules from neural networks for time series prediction. Copyright 2014 ACM.
In this paper, four different block matching algorithms using motion estimation are evaluated where the effects of the macro block size used will be reviewed to find the best algorithm among them is scrutinized to det...
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In this paper, four different block matching algorithms using motion estimation are evaluated where the effects of the macro block size used will be reviewed to find the best algorithm among them is scrutinized to determine the most optimal algorithm. Four different block matching algorithms are considered and implemented. Each algorithm is evaluated using different movies from the TRANS database [11] and comparisons are made through the Peak Signal to Noise Ratio (PSNR) and search points per macro block (i.e. computation time) for different sizes of macro blocks and search areas. The results suggest that among all the evaluated algorithms, ARPS has the best PSNR based on computation time.
Consistency checking plays a central role in qualitative spatial and temporal reasoning. Given a set of variables V, and a set of constraints Γ taken from a qualitative calculus (e.g. the Interval Algebra (IA) or RCC...
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A novel heuristic model-based optimal scheduling algorithm is proposed in this paper to operate heating and cooling type home appliances connected to smart grids where the price of the electrical energy is known in ad...
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The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these...
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The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and *** address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making *** this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in *** theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy *** tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) *** function served as input for the SOA ***,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and *** extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the *** study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain *** simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.
Activities that require students to collaborate, share solutions, review each others' work, or create materials explicitly for the use of other students have been shown to be beneficial not only to students' l...
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