Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant;however, this trend appears to be changing so...
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Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant;however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising;however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to t
Agriculture plays a vital role in the Indian *** recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic *** the same time,crop yield predict...
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Agriculture plays a vital role in the Indian *** recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic *** the same time,crop yield prediction was based on several features like area,irrigation type,temperature,*** recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction *** this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)*** proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop *** the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of ***,random forest(RF)tech-nique was executed for predicting the crop yield *** reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark ***-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.
With the increasing complexity of graph query processing tasks, it is difficult for users to obtain the accurate cardinality before or during the execution of query tasks. Accurate estimate query cardinality is crucia...
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In the current medical implications, one of the leading ocular diseases is Glaucoma which majorly damage the Optic Nerve Head (ONH) of the eye retina. The intraocular pressure of the eye leads to glaucoma, which may l...
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This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foragin...
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This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.
Handwritten character segmentation plays a pivotal role in the performance of Optical Character Recognition (OCR) systems. This paper introduces an innovative approach to enhancing segmentation accuracy using Region-B...
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To serve a convenient healthcare network, storing medical images and diagnosis records in the cloud is a straightforward solution. Encrypting the medical images before uploading them to the cloud is a trivial strategy...
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The concept of mutual-visibility in graphs has been recently introduced. If X is a subset of vertices of a graph G, then vertices u and v are X-visible if there exists a shortest u, v-path P such that V (P) ∩ X ⊆ {u,...
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The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(...
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The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.
Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least t...
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Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.
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