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.
With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the ba...
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With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every ***,the fraud cases have also increased causing the loss of money to the ***,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in ***,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance *** this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional ***,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.
This paper investigates an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery and sensing data gathering services. Two tenants provide services to their clients by sharing a comm...
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This paper considers an integrated sensing and communication (ISAC) system framework, in which an aerial eavesdropper poses the threat to intercept the downlink communication from a base station to a set of users. The...
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Optical resonances can increase the sensitivity of measurements to material perturbations and also accelerate photochemical reactions. Here, we show that these two effects can be combined multiplicatively, to enhance ...
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Optical resonances can increase the sensitivity of measurements to material perturbations and also accelerate photochemical reactions. Here, we show that these two effects can be combined multiplicatively, to enhance the detection via weak or low-concentration photochemical reactions far beyond what could previously be attained. For an optical resonance with quality factor Q, the sensitivity of our detection scheme is enhanced by ∼Q2 (where ∼ denotes approximate proportionality), as demonstrated by both theoretical arguments and numerical simulations of a simple optical-grating resonance coupled with reaction-diffusion equations. Such an approach opens a door to further improvements by careful design of the resonance: even a three-parameter optimization of the grating resonance yields an additional ≈7 times improvement.
Protein nanowires harvested from microbes G. sulfurreducens are a renewable, green electronic material. Electronic devices made from protein nanowires show novel or improved functions in energy harvesting, computing a...
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"The right to be forgotten" ensured by laws for user data privacy becomes increasingly important. Machine unlearning aims to efficiently remove the effect of certain data points on the trained model paramete...
In response to the increasing environmental threats, ForeScanGuard emerges as a vital forest monitoring system. Integrating advanced sensors and a proactive anomaly detection algorithm, it employs a 5-channel fire sen...
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In the past few years, image processing has been widely adopted for symptom diagnosis of medical application. To achieve accurate analysis, the medical applications require high quality image for applying to the sympt...
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Machine unlearning has raised significant interest with the adoption of laws ensuring the "right to be forgotten". Researchers have provided a probabilistic notion of approximate unlearning under a similar d...
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