The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-F...The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.展开更多
Data security is a significant issue in cloud storage systems. After outsourcing data to cloud servers, clients lose physical control over the data. To guarantee clients that their data is intact on the server side, s...Data security is a significant issue in cloud storage systems. After outsourcing data to cloud servers, clients lose physical control over the data. To guarantee clients that their data is intact on the server side, some mechanism is needed for clients to periodically check the integrity of their data. Proof of retrievability （PoR） is designed to ensure data integrity. However, most prior PoR schemes focus on static data, and existing dynamic PoR is inefficient. In this paper, we propose a new version of dynamic PoR that is based on a B＋ tree and a Merkle hash tree. We propose a novel authenticated data structure, called Cloud Merkle B＋ tree （CMBT）. By combining CMBT with the BES signature, dynamic operations such as insertion, deletion, and modification are supported. Compared with existing PoR schemes, our scheme improves worst-case overhead from O（n） to O（log n）.展开更多
miRNAs are non-coding small RNAs that involve diverse biological processes. Until now, little is known about their roles in plant drought resistance. Physcomitrella patens is highly tolerant to drought; however, it is...miRNAs are non-coding small RNAs that involve diverse biological processes. Until now, little is known about their roles in plant drought resistance. Physcomitrella patens is highly tolerant to drought; however, it is not clear about the basic biology of the traits that contribute P. patens this important character. In this work, we discovered 16 drought stress-associated miRNA （DsAmR） families in P. patens through computational analysis. Due to the possible discrepancy of expression periods and tissue distributions between potential DsAmRs and their targeting genes, and the existence of false positive results in computational identification, the prediction results should be examined with further experimental validation. We also constructed an miRNA co-regulation network, and identi- fied two network hubs, miR902a-Sp and miR414, which may play important roles in regulating drought-resistance traits. We distributed our results through an online database named ppt-miRBase, which can be accessed at http：/Poioinfor.cnu.edu.cn/ppt_miRBase/index.php. Our methods in finding DsAmR and miRNA co-regulation network showed a new direction for identifying miRNA functions.展开更多
A G-Frobenius graph F, as defined by Fang, Li, and Praeger, is a connected orbital graph of a Frobenius group G = K × H with Frobenius kernel K and Frobenius complement H. F is also shown to be a Cayley graph, F ...A G-Frobenius graph F, as defined by Fang, Li, and Praeger, is a connected orbital graph of a Frobenius group G = K × H with Frobenius kernel K and Frobenius complement H. F is also shown to be a Cayley graph, F = Cay（K, S） for K and some subset S of the group K. On the other hand, a network N with a routing function R, written as （N, R）, is an undirected graph N together with a routing R which consists of a collection of simple paths connecting every pair of vertices in the graph. The edge-forwarding index π（N） of a network （N, R）, defined by Heydemann, Meyer, and Sotteau, is a parameter to describe tile maximum load of edges of N. In this paper, we study the edge-forwarding indices of Frobenius graphs. In particular, we obtain the edge-forwarding index of a G-Frobenius graph F with rank（G） ≤ 50.展开更多
This paper describes some experiments of analogical learning and automated rule construction.The present investigation focuses on knowledge acquisition,learning by analogy,and knowledge retention. The developed system...This paper describes some experiments of analogical learning and automated rule construction.The present investigation focuses on knowledge acquisition,learning by analogy,and knowledge retention. The developed system initially learns from scratch,gradually acquires knowledge from,its environment through trial-and-error interaction,incrementally augments its knowledge base,and analogically solves new tasks in a more efficient and direct manner.展开更多
文摘The increasing integration of the Internet of Things (IoT) in healthcare is revolutionizing patient monitoring and disease prediction. This paper presents a machine learning (ML)-based framework using Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict diabetes. The proposed system leverages IoT data to monitor key health parameters, including glucose levels, blood pressure, and age, offering real-time diagnostics for diabetes patients. The dataset used in this study was obtained from the UCI repository and underwent preprocessing, feature selection, and classification using the ANFIS model. Comparative analysis with other machine learning algorithms, such as Support Vector Machines (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), demonstrates that the proposed method achieves superior predictive performance. The experimental results show that the ANFIS model achieved an accuracy of 95.5%, outperforming conventional models, and providing more reliable decision-making in clinical settings. This study highlights the potential of combining IoT with machine learning to improve predictive healthcare applications, emphasizing the need for real-time patient monitoring systems.
基金supported in part by the US National Science Foundation under grant CNS-1115548 and a grant from Cisco Research
文摘Data security is a significant issue in cloud storage systems. After outsourcing data to cloud servers, clients lose physical control over the data. To guarantee clients that their data is intact on the server side, some mechanism is needed for clients to periodically check the integrity of their data. Proof of retrievability （PoR） is designed to ensure data integrity. However, most prior PoR schemes focus on static data, and existing dynamic PoR is inefficient. In this paper, we propose a new version of dynamic PoR that is based on a B＋ tree and a Merkle hash tree. We propose a novel authenticated data structure, called Cloud Merkle B＋ tree （CMBT）. By combining CMBT with the BES signature, dynamic operations such as insertion, deletion, and modification are supported. Compared with existing PoR schemes, our scheme improves worst-case overhead from O（n） to O（log n）.
基金supported by Beijing Municipal Education CommissionScience and Technology Development Project (Grant No. KM200710028013)PHR Project (Grant No. PHR201008078)
文摘miRNAs are non-coding small RNAs that involve diverse biological processes. Until now, little is known about their roles in plant drought resistance. Physcomitrella patens is highly tolerant to drought; however, it is not clear about the basic biology of the traits that contribute P. patens this important character. In this work, we discovered 16 drought stress-associated miRNA （DsAmR） families in P. patens through computational analysis. Due to the possible discrepancy of expression periods and tissue distributions between potential DsAmRs and their targeting genes, and the existence of false positive results in computational identification, the prediction results should be examined with further experimental validation. We also constructed an miRNA co-regulation network, and identi- fied two network hubs, miR902a-Sp and miR414, which may play important roles in regulating drought-resistance traits. We distributed our results through an online database named ppt-miRBase, which can be accessed at http：/Poioinfor.cnu.edu.cn/ppt_miRBase/index.php. Our methods in finding DsAmR and miRNA co-regulation network showed a new direction for identifying miRNA functions.
基金The first two authors are supported by the Natural Science Foundation(No.10571005)and RFDP of China
文摘A G-Frobenius graph F, as defined by Fang, Li, and Praeger, is a connected orbital graph of a Frobenius group G = K × H with Frobenius kernel K and Frobenius complement H. F is also shown to be a Cayley graph, F = Cay（K, S） for K and some subset S of the group K. On the other hand, a network N with a routing function R, written as （N, R）, is an undirected graph N together with a routing R which consists of a collection of simple paths connecting every pair of vertices in the graph. The edge-forwarding index π（N） of a network （N, R）, defined by Heydemann, Meyer, and Sotteau, is a parameter to describe tile maximum load of edges of N. In this paper, we study the edge-forwarding indices of Frobenius graphs. In particular, we obtain the edge-forwarding index of a G-Frobenius graph F with rank（G） ≤ 50.
文摘This paper describes some experiments of analogical learning and automated rule construction.The present investigation focuses on knowledge acquisition,learning by analogy,and knowledge retention. The developed system initially learns from scratch,gradually acquires knowledge from,its environment through trial-and-error interaction,incrementally augments its knowledge base,and analogically solves new tasks in a more efficient and direct manner.
