Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for n...
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Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.
In innovation world, fog and edge computing is extremely near the client which means making the processing capacities extremely nearer to the customer or user. The mushrooming of IoT devices and usage is increasing da...
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Background: Consumer generated media (CGM), such as social networking services (SNS) and review sites, are used by many people;however, these sites rely on the voluntary activity of users to prosper, garnering the psy...
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Background: Consumer generated media (CGM), such as social networking services (SNS) and review sites, are used by many people;however, these sites rely on the voluntary activity of users to prosper, garnering the psychological rewards of feeling connected with other people through comments and reviews received online. To attract more users, some CGM have introduced monetary rewards (MR) for publishing activity. However, the effect of MR on the article posting strategies of users, especially frequency and quality, has not been fully analyzed. Usui et al. [1] proposed a game theoretical-model, the SNS-norms game with monetary reward and article quality (SNS-NG/MQ), and investigated the dominant behavior of users by introducing a few strategies for providing MR (MR strategies) using the genetic algorithm (GA). They found that although MR increases the frequency of article posts, the positive or negative impact on article quality depends on the MR strategy. However, the dominant strategies determined by the naive GA are almost identical for all users. We believe that appropriate strategies for CGM depend on the standpoints of users, such as normal users or influencers who have a large number of followers. However, such differences were ignored in their study although exploring the individual behaviors of users with different attitudes is crucial for CGM to infer the overall behavioral structure of all users. Purpose and Method: The purpose of this study is to investigate the effect of MR on individual users by considering the differences in dominant strategies with respect to user standpoints. To this end, to determine the individual strategies of users in SNS-NG/MQ, we applied multiple-world GA (MWGA) [2] instead of GA. MWGA generates several copies of a CGM network with nodes as agents that correspond to users, and each agent in the multiple worlds selects different behavioral strategies to interact with its neighboring agents. Then, the agent with the larger reward
Our work focuses, on designing and proposing a force estimation sensor system for Geological phenomena data collection and analysis, based on Biomimetic. In this sense, biomimetic term, is used, as proposed by Schmitt...
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Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses b...
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Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses by genome sequence is a biological problem that benefits from computational *** National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the *** research aims to classify the different types of rotavirus genome sequences available at the ***,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing *** on some significant results,the number of features used for classification is reduced to *** classification shall be carried out in two *** first phase of classification shall select only two *** data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three *** data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the *** performance of the classifier is analyzed using the test data ***,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare *** results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.
Many pregnant women still do not want to check their pregnancy to recognize the signs of high-risk pregnant women. High-risk pregnancies refer to abundant women with several risky conditions requiring special care dur...
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Education plays a pivotal role in individual development, imparting growth, values, and cultural understanding. In the realm of education, university education emerges as a transformative phase crucial for professiona...
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ISBN:
(数字)9798350358155
ISBN:
(纸本)9798350358162
Education plays a pivotal role in individual development, imparting growth, values, and cultural understanding. In the realm of education, university education emerges as a transformative phase crucial for professional life. The selection of the right course during these formative years significantly shapes one's life trajectory. Amidst the complexities of this decision-making stage, exacerbated by societal pressures, pre-tertiary students often grapple with confusion. This research addresses the unique challenges faced by pre-tertiary students through the introduction of a Web-Based Admission Recommender System. Unlike existing systems, our recommender system empowers students to autonomously make well-informed decisions about their course of study, considering their distinctive abilities. The system integrates three crucial parameters: preferred core subjects’ combination, Intelligence Quotient, and Career Interest. Implemented with the Catboost Classification utilizing the Gradient Boosting Algorithm, and featuring a user interface designed with Bootstrap 3, Python, and Flask, the system underwent rigorous testing with primary data from 346 secondary school students in their final year. The evaluation showcased commendable accuracy, with a notable 86.71% accuracy rate, 74.4% precision, 80% recall, and an 83% f1 score rate. This research makes distinctive contributions to the field by significantly enhancing the decision-making process for pretertiary students. The recommender system emerges as a reliable tool, uniquely positioned to guide students in selecting courses aligned with their individual capabilities and aspirations. By addressing the nuanced needs of pre-tertiary students, our research sets a new standard in personalized educational guidance.
With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive...
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ISBN:
(数字)9798331508685
ISBN:
(纸本)9798331519476
With advancements in artificial intelligence, it is natural for the health industry to focus on IOT devices. AI could promote a paradigm shift in how doctors and patients interact through immediate updates, predictive features, and remote access. The focus of this study is the outreach and employment aspects revolving around AI-enabled health care, with special attention to mHealth solutions. We searched through published data and came across 112 relevant studies identifying keywords such as accessibility, empowerment, and workforce. One of the most interesting findings was the fact that AI targeting mobile applications has the possibility of increasing access to health services by approximately 60 percent for rural areas, as well as increasing the patient cooperative rate by approximately 45%, where a predictive health tracking device has been utilized. We proposed two new measures: the Physical Activity and Nutrition Index (PANI) and Health Status Index (HSI), allowing for better diabetes risk predictions with a 0.82 AUC-ROC and better generalizability than traditional baselines, with an improvement of 12%. When identifying the best available ML models, including Logistic Regression, Random Forest, XGBoost, LightGBM, and Bayesian Neural Networks, we found that the hybrid Random Forest + Linear SVC model had the best performance with recall and accuracy of 0.77 and 0.72, respectively. The ability of the model to demonstrate both interpretability and specificity was evident. It was also observed that synthetic data augmentation (SMOTE) increased the recall for minority class predictions by 18% but did not reduce specificity.
The rapid evolution of autonomous vehicles (AVs) presents significant opportunities for enhancing transportation safety and efficiency. However, with increasing connectivity and complex electronic systems, AVs also be...
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ISBN:
(数字)9798331540906
ISBN:
(纸本)9798331540913
The rapid evolution of autonomous vehicles (AVs) presents significant opportunities for enhancing transportation safety and efficiency. However, with increasing connectivity and complex electronic systems, AVs also become vulnerable to cyberattacks. This paper investigates cybersecurity challenges in the realm of AVs, highlighting the role of artificial intelligence (AI), specifically Large Language Models (LLMs), in exploiting vulnerabilities. We analyze various attack vectors, including Controller Area Network (CAN) manipulation, Bluetooth vulnerabilities, and Key Fob hacking, emphasizing the need for proactive cybersecurity measures. Recent incidents, such as the remote compromise of various vehicle models, underscore the urgent need for robust security solutions in the automotive industry. By leveraging LLMs, attackers can craft sophisticated cyber threats targeting AVs, posing risks to both safety and privacy. We introduce HackerGPT, a customized LLM tailored for cyber attack generation, and demonstrate attacks on virtual CAN networks, Bluetooth systems, and Key Fobs. At the same time, our experiments reveal successful compromises in certain vehicle models; limitations exist, particularly in vehicles with advanced encryption and robust signal transmission protocols. However, this research underscores the broader need for increased awareness and proactive security measures in the automotive sector. Our findings aim to contribute significantly to the ongoing discourse on automotive cybersecurity, offering actionable insights for manufacturers and cybersecurity professionals to safeguard the future of mobility.
Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (U...
Collaboration of agents in a natural swarm enables the accomplishment of tasks that would be difficult or impossible for a single agent to complete alone. For example, a swarm of autonomous Unmanned Aerial Vehicles (UAVs) enables the collaborative sensing of bulky loads for transportation over impassable terrains when the load weighs several times more than each UAV. In this work, we propose a hierarchical algorithmic architecture that supports the search and coverage of various unknown payload profiles for subsequent transportation. The grasping formation of UAVs over the payload emerges from the synthetic behaviours in the architecture without any path planning. Experiments show that our proposed design can be successfully applied in searching and coverage of various loads and has been validated in the real world through the use of Crazyflie micro-UAVs. Furthermore, the proposed grasping formation satisfies static equilibrium thereby reducing orientation changes in the load-swarm system during transportation.
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