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Bootstrap Aggregative Mean-Shift SQL Query Clustering (BAMSQLQC) technique is to identify the anti-patterns in the big data logs with a minimal false-positive rate. BAMSQLQC technique collects numbers of patterns (i.e. queries) from input SQL query big data logs and creates bootstrap big data samples by using the patterns in the given dataset. The BAMSQLQC technique constructs several weak clusters for each pattern in a bootstrap sample. The clustering output of all weak mean-shift clustering is combined into a strong cluster by using the voting scheme to efficiently group similar patterns together with a lesser false-positive rate. The BAMSQLQC technique conducts the experimental results using metrics such as anti-patterns detection accuracy, false-positive rate, time and space complexity. The results show that the BAMSQLQC technique can increase the accuracy and reduce the time complexity of anti-patterns discovery for effective big data analytics in 5G networks compared to existing techniques.
Vinothsaravanan Ramakrishnan; Palanisamy Chenniappan; Rajesh Kumar Dhanaraj; Ching-Hsien Hsu; Yingyuan Xiao; Fadi Al-Turjman. Bootstrap aggregative mean shift clustering for big data anti-pattern detection analytics in 5G/6G communication networks. Computers & Electrical Engineering 2021, 95, 107380 .
AMA StyleVinothsaravanan Ramakrishnan, Palanisamy Chenniappan, Rajesh Kumar Dhanaraj, Ching-Hsien Hsu, Yingyuan Xiao, Fadi Al-Turjman. Bootstrap aggregative mean shift clustering for big data anti-pattern detection analytics in 5G/6G communication networks. Computers & Electrical Engineering. 2021; 95 ():107380.
Chicago/Turabian StyleVinothsaravanan Ramakrishnan; Palanisamy Chenniappan; Rajesh Kumar Dhanaraj; Ching-Hsien Hsu; Yingyuan Xiao; Fadi Al-Turjman. 2021. "Bootstrap aggregative mean shift clustering for big data anti-pattern detection analytics in 5G/6G communication networks." Computers & Electrical Engineering 95, no. : 107380.
Crowdfunding is an emerging internet platform that provides financial support for people in need. With the development of crowdfunding platforms, the number of projects released on these platforms is increasing, and thus it is challenging for lenders to find suitable crowdfunding projects quickly. A personalized recommender system is helpful for solving this problem. To this end, a crowdfunding project recommendation approach is proposed in this work for predicting how likely a lender is to fund a project. Specifically, given the fact that the lenders consider not only their interests but also the benefits they can obtain when funding, we first design a module that predicts the success rate of the projects. Then, a feature interaction learning model based on deep learning, called the crowdfunding feature interaction learning model, is proposed. It integrates all features, automatically recognizes the importance of features, and learns the feature interaction. This allows us to identify combination of useful features more accurately and provide effective predictions. Extensive experiments are conducted on a dataset collected from a real-world crowdfunding platform, and the results show that our approach has a 4.57% improvement on AUC (area under the curve) compare with the state-of-the-art methods.
Yingyuan Xiao; Chichang Liu; Wenguang Zheng; Hongya Wang; Ching-Hsien Hsu. A feature interaction learning approach for crowdfunding project recommendation. Applied Soft Computing 2021, 112, 107777 .
AMA StyleYingyuan Xiao, Chichang Liu, Wenguang Zheng, Hongya Wang, Ching-Hsien Hsu. A feature interaction learning approach for crowdfunding project recommendation. Applied Soft Computing. 2021; 112 ():107777.
Chicago/Turabian StyleYingyuan Xiao; Chichang Liu; Wenguang Zheng; Hongya Wang; Ching-Hsien Hsu. 2021. "A feature interaction learning approach for crowdfunding project recommendation." Applied Soft Computing 112, no. : 107777.
Edge computing plays a critical role in IoT as it potentially minimized the computation tasks response latency demanded by time-critical IoT applications. The growth of IoT users with high demanded computation power as well as ultra-low latency tasks may cause the performance degradation. One way to minimize the end-to-end (E2E) latency is to form horizontal edge federation (HEF) so that the computation resources can be shared with each participating edge node. Achieving ultra-low latency in HEF-IoT ecosystem involves setting two factor: resource allocation and task dispatching. This two factor interact with each other yet feasible solutions must provide satisfactory service level to meet latency constraints demanded by target applications. In this paper, we formulate it as E2E latency minimization problem and proposed a two-phase iterative (TPI) approach. The TPI method alternately determines optimal task dispatching and computation resource allocation. We exploit bin packing problem and, genetic algorithm (GA) to determine the edge nodes, and the required computation resources. The simulation results show that by using TPI approach, we can achieve more throughput, minimum E2E latency and optimum number of required edge nodes.
Hojjat Baghban; Ching-Yao Huang; Ching-Hsien Hsu. Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation. Multimedia Tools and Applications 2021, 1 -18.
AMA StyleHojjat Baghban, Ching-Yao Huang, Ching-Hsien Hsu. Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation. Multimedia Tools and Applications. 2021; ():1-18.
Chicago/Turabian StyleHojjat Baghban; Ching-Yao Huang; Ching-Hsien Hsu. 2021. "Latency minimization model towards high efficiency edge-IoT service provisioning in horizontal edge federation." Multimedia Tools and Applications , no. : 1-18.
Presents corrections to author affiliation information in the above named paper.
Ibrahim A. Elgendy; Wei-Zhe Zhang; Chuan-Yi Liu; Ching-Hsien Hsu. Correction to “An Efficient and Secured Framework For Mobile Cloud Computing”. IEEE Transactions on Cloud Computing 2021, 9, 844 -844.
AMA StyleIbrahim A. Elgendy, Wei-Zhe Zhang, Chuan-Yi Liu, Ching-Hsien Hsu. Correction to “An Efficient and Secured Framework For Mobile Cloud Computing”. IEEE Transactions on Cloud Computing. 2021; 9 (2):844-844.
Chicago/Turabian StyleIbrahim A. Elgendy; Wei-Zhe Zhang; Chuan-Yi Liu; Ching-Hsien Hsu. 2021. "Correction to “An Efficient and Secured Framework For Mobile Cloud Computing”." IEEE Transactions on Cloud Computing 9, no. 2: 844-844.
Adaptation and resilience practitioners lack guidance on how to understand and manage extreme climate risk using the data available. We present a methodological framework to integrate the satellite as well as location based data sets to estimate extreme climate risk. The framework, in detail, has been demonstration using a study carried out to quantify extreme rainfall risks in India incorporating the influence of global (large scale oscillations) as well as local factors (population, infrastructure, economic activity) in a probabilistic model. We use nonstationary extreme value theory along with Bayesian uncertainty analysis to model the time varying influence of oscillations such as El Niño/Southern Oscillation, Indian Ocean Dipole, and North Atlantic Oscillation in augmenting high rainfall risks in 637 districts across 29 states of India. It is found that at least 50% of the districts in 8 out of 29 states are at high risk. Extreme risk is observed in 198 (~31%) and 249 (~39%) districts caused by heavy downpour and extremely long wet spells, respectively. This study provides a framework to identify local implications of global factors and is aimed at supporting policy makers in framing extreme rainfall-induced disaster risk reduction strategies.
Srinidhi Jha; Manish K. Goyal; Brij B. Gupta; Ching‐Hsien Hsu; Eric Gilleland; Jew Das. A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems. International Journal of Intelligent Systems 2021, 1 .
AMA StyleSrinidhi Jha, Manish K. Goyal, Brij B. Gupta, Ching‐Hsien Hsu, Eric Gilleland, Jew Das. A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems. International Journal of Intelligent Systems. 2021; ():1.
Chicago/Turabian StyleSrinidhi Jha; Manish K. Goyal; Brij B. Gupta; Ching‐Hsien Hsu; Eric Gilleland; Jew Das. 2021. "A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems." International Journal of Intelligent Systems , no. : 1.
Deep neural network (DNN) has become increasingly popular in industrial IoT scenarios. Due to high demands on computational capability, it is hard for DNN-based applications to directly run on intelligent end devices with limited resources. Computation offloading technology offers a feasible solution by offloading some computation-intensive tasks to the cloud or edges. Supporting such capability is not easy due to two aspects: (1) Adaptability: offloading should be dynamically occur among computation nodes. (2) Effectiveness: it needs to be determined which parts are worth offloading. This paper proposed a novel approach, called DNNOff. For a given DNN-based application, DNNOff first rewrites the source code to implement a special program structure supporting on-demand offloading, and at runtime, automatically determines the offloading scheme. We evaluated DNNOff on a real-world intelligent application, with three DNN models. Our results show that, compared with other approaches, DNNOff saves response time by 12.4%-66.6% on average.
Xing Chen; Ming Li; Hao Zhong; Yun Ma; Ching-Hsien Hsu. DNNOff: Offloading DNN-based Intelligent IoT Applications in Mobile Edge Computing. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.
AMA StyleXing Chen, Ming Li, Hao Zhong, Yun Ma, Ching-Hsien Hsu. DNNOff: Offloading DNN-based Intelligent IoT Applications in Mobile Edge Computing. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.
Chicago/Turabian StyleXing Chen; Ming Li; Hao Zhong; Yun Ma; Ching-Hsien Hsu. 2021. "DNNOff: Offloading DNN-based Intelligent IoT Applications in Mobile Edge Computing." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.
As an important part of the new infrastructure, the cloud data center is developing rapidly, and its energy consumption problem is becoming more and more prominent. Therefore, research on energy-saving technologies for cloud data centers has attracted widespread attention from academia and industry. Some studies have reviewed energy-saving optimization methods and technologies for data centers, but recently, many state-of-the-art optimization methods of energy consumption and energy-saving technologies have sprung out, which are still worth analyzing and discussing. Depending on the in-depth investigation and analysis of related research status, this article firstly focuses on analyzing and discussing the energy-saving technologies of the two components: IT equipment and cooling systems, both of which bring about the largest energy consumption in cloud data centers. As for IT equipment, its energy-saving technologies mainly include the energy saving of servers, storage systems, and network systems. While as for cooling systems, airflow organization in the computer room, thermal-aware scheduling technology, and other new energy-saving technologies are involved. Secondly, on the basis of analyzing the energy-saving technologies of the two major components, a new optimization scheme of energy consumption for the jointing computing system and cooling system is explained. Throughout this work, various energy-saving strategies and technologies have been described and compared. Finally, the future trends and development directions of energy saving for data centers are further promoted, such as integral optimization of energy consumption jointing multiple components, energy saving using artificial intelligence methods, energy saving based on novel hardware equipment, hybrid cooling energy saving, and comprehensive energy conservation with various energy technologies.
Huiwen Cheng; Bo Liu; Weiwei Lin; Zehua Ma; Keqin Li; Ching-Hsien Hsu. A survey of energy-saving technologies in cloud data centers. The Journal of Supercomputing 2021, 1 -36.
AMA StyleHuiwen Cheng, Bo Liu, Weiwei Lin, Zehua Ma, Keqin Li, Ching-Hsien Hsu. A survey of energy-saving technologies in cloud data centers. The Journal of Supercomputing. 2021; ():1-36.
Chicago/Turabian StyleHuiwen Cheng; Bo Liu; Weiwei Lin; Zehua Ma; Keqin Li; Ching-Hsien Hsu. 2021. "A survey of energy-saving technologies in cloud data centers." The Journal of Supercomputing , no. : 1-36.
Software defined network (SDN) architectures are assimilated with vehicular communication networks in order to improve the real-time application support for the driving users. Internet of vehicles (IoV) paradigm provides information-centric application support for the road-side users. Information sharing through the road-side units (RSUs) influences the application services due to the frequent change in physical attributes of the vehicles. Considering the application oriented services and information handling in IoV, this article introduces information-centric content management framework (ICMF) for effective information utilization in the vehicular networks. This framework performs data acquisition, smoothing and management process for effective information analysis and better offloading. The proposed framework incorporates the functions of linear vector quantization for classifying acquired information and segregating it for maximum utilization. This quantization is recurrent in both continuous and alternating learning process to improve the reliability of information handling and management. The performance of the proposed framework is verified using simulations and the results prove its efficiency. The proposed framework is found to maximize resource utilization and offloading ratio with less analysis time and overhead. The simulation is verified for the varying density of vehicles, offloading ratio, and communication time, information utilization.
Gunasekaran Manogaran; Vijayalakshmi Saravanan; Ching-Hsien Hsu. Information-Centric Content Management Framework for Software Defined Internet of Vehicles Towards Application Specific Services. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -9.
AMA StyleGunasekaran Manogaran, Vijayalakshmi Saravanan, Ching-Hsien Hsu. Information-Centric Content Management Framework for Software Defined Internet of Vehicles Towards Application Specific Services. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-9.
Chicago/Turabian StyleGunasekaran Manogaran; Vijayalakshmi Saravanan; Ching-Hsien Hsu. 2021. "Information-Centric Content Management Framework for Software Defined Internet of Vehicles Towards Application Specific Services." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-9.
Community detection algorithms (CDAs) are aiming to group nodes based on their connections and play an essential role in the complex system analysis. However, for privacy reasons, we may want to prevent communities or a group of nodes in the complex industrial network from being discovered in some instances, leading to the topics on community deception. In this paper, we introduce and formalize two intelligent community deception methods to conceal the nodes from various CDAs. We used node‐based matrices, persistence and safeness scores, to formalize the optimization problems to confound the CDAs. The persistence score is used to destabilize the constant communities in the network while the safeness score is used to assess the level of hiding of a node from CDAs. The objective functions aim to minimize the persistence score and maximize the safeness score of the nodes in the network. From the simulation results, it can be analyzed that the proposed strategies are intelligently concealing the community information in the complex industrial system.
Suchi Kumari; Riteshkumar Jayprakash Yadav; Suyel Namasudra; Ching‐Hsien Hsu. Intelligent deception techniques against adversarial attack on the industrial system. International Journal of Intelligent Systems 2021, 36, 2412 -2437.
AMA StyleSuchi Kumari, Riteshkumar Jayprakash Yadav, Suyel Namasudra, Ching‐Hsien Hsu. Intelligent deception techniques against adversarial attack on the industrial system. International Journal of Intelligent Systems. 2021; 36 (5):2412-2437.
Chicago/Turabian StyleSuchi Kumari; Riteshkumar Jayprakash Yadav; Suyel Namasudra; Ching‐Hsien Hsu. 2021. "Intelligent deception techniques against adversarial attack on the industrial system." International Journal of Intelligent Systems 36, no. 5: 2412-2437.
Cancer is a kind of non-communicable disease, progresses with uncontrolled cell growth in the body. The cancerous cell forms a tumor that impairs the immune system, causes other biological changes to malfunction. The most common kinds of cancer are breast, prostate, leukemia, lung, and colon cancer. The presence of the disease is identified with the proper diagnosis. Many screening procedures are suggested to find the presence of the condition under different stages. Medical practitioners further analyze these electronic health records to diagnose and treat the individual. In some cases, misdiagnosis can happen due to manual error or misinterpretation of the data. To avoid these issues, this paper presents an effective computer-aided diagnosis system supported by intelligence learning models. A machine learning-based feature modeling is proposed to improve predictive performance. From the University of California, Irvine repository, breast, cervical, and lung cancer datasets are accessed to conduct this experimental study. Supervised learning algorithms are employed to train and validate the optimal features reduced by the proposed system. Using the 10-Fold cross-validation method, the trained and performance model is evaluated with validation metrics such as accuracy, f-score, precision, and recall. The study's outcome attained 99.62%, 96.88%, and 98.21% accuracy on breast, cervical, and lung cancer datasets, respectively, which exhibits the proposed system's efficacy. Moreover, this system acts as a miscellaneous tool for capturing the pattern from many clinical trials for multiple types of cancer disease.
Ching-Hsien Hsu; Xing Chen; Weiwei Lin; Chuntao Jiang; Youhong Zhang; Zhifeng Hao; Yeh-Ching Chung. Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning. Measurement 2021, 175, 109145 .
AMA StyleChing-Hsien Hsu, Xing Chen, Weiwei Lin, Chuntao Jiang, Youhong Zhang, Zhifeng Hao, Yeh-Ching Chung. Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning. Measurement. 2021; 175 ():109145.
Chicago/Turabian StyleChing-Hsien Hsu; Xing Chen; Weiwei Lin; Chuntao Jiang; Youhong Zhang; Zhifeng Hao; Yeh-Ching Chung. 2021. "Effective multiple cancer disease diagnosis frameworks for improved healthcare using machine learning." Measurement 175, no. : 109145.
Present day world have evolved from traditional environment to smart industries using IoT scheme which in turn forms Industrial Internet of Things (IIoT), which significantly elaborated by providing enhance integration using smart communication through IoT based sensors. IIoT has been providing cost reduction and enhancement in technology by bringing availability, flexibility and data sharing through real time scenario. Despite being unsecure environment of cloud, the privacy of data transfer and information confidentiality is guaranteed. In this context, this work presents a Public Key Encryption with Equality Test based on DLP with double decomposition problems over near‐ring. Computation Diffie‐Hellman is utilized in algebraic structure which involves DLP with Double Decomposition problem for proposing a Public Key Encryption with Equality Test which provides more security to the scheme. The proposed method is highly secure and it solves the problem of quantum algorithm attacks in IIoT systems. Further, the suggested system is significantly secure and it prevents the chosen‐ciphertext attack in type‐I rival and it is indistinguishable against the random oracle model for the type‐II rival. The recommended scheme is highly secure and the security analysis measures are comparatively stronger than existing techniques. Search time of the proposed scheme is 150 milliseconds for which the number of attributes is 50 and when comparing to the decryption time of the proposed model which is lower when compared to other existing scheme for 50 attributes.
Ganesh Gopal Deverajan; V. Muthukumaran; Ching‐Hsien Hsu; Marimuthu Karuppiah; Yeh‐Ching Chung; Ying‐Huei Chen. Public key encryption with equality test for Industrial Internet of Things system in cloud computing. Transactions on Emerging Telecommunications Technologies 2021, 1 .
AMA StyleGanesh Gopal Deverajan, V. Muthukumaran, Ching‐Hsien Hsu, Marimuthu Karuppiah, Yeh‐Ching Chung, Ying‐Huei Chen. Public key encryption with equality test for Industrial Internet of Things system in cloud computing. Transactions on Emerging Telecommunications Technologies. 2021; ():1.
Chicago/Turabian StyleGanesh Gopal Deverajan; V. Muthukumaran; Ching‐Hsien Hsu; Marimuthu Karuppiah; Yeh‐Ching Chung; Ying‐Huei Chen. 2021. "Public key encryption with equality test for Industrial Internet of Things system in cloud computing." Transactions on Emerging Telecommunications Technologies , no. : 1.
Vehicular Ad hoc Networks (VANETs) that are considered as a subset of Mobile Ad hoc Networks (MANETs) can be applied in the field of transportation especially in Intelligent Transportation Systems (ITS). The routing process in these networks is a challenging task due to rapid topology changes, high vehicle mobility and frequent disconnection of links. Therefore, developing an efficient routing protocol that satisfies restriction of delay and minimum overhead is faced with many difficulties and limitations. Also, the detection of malicious vehicles is a significant task in VANETs. To address these issues, using Unmanned Aerial Vehicles (UAVs) can be helpful to cope with these limitations. In this paper, operation of UAVs in ad hoc mode and their cooperation with vehicles in VANETs are studied to help in the process of routing and detection of malicious vehicles. A routing protocol named VRU is proposed that includes two distinct ways of routing of data: (1) delivering packets of data between vehicles with the help of UAVs using a protocol named VRU_vu, and (2) routing packet of data between UAVs using a protocol named VRU_u. The NS-2.35 simulator under Linux Ubuntu 12.04 is utilized in order to appraise the performance of VRU routing components in an urban scenario. Also, VanetMobiSim generator of mobility and MobiSim are used to produce the motions of vehicles and to produce the motions of UAVs, respectively. The performance analysis displays that VRU protocol can improve the packet delivery ratio by 16% and detection ratio by 7% compared to other reviewed routing protocol. Also, VRU protocol decreases end-to-end delay by an average of 13% and overhead by 40%.
Hamideh Fatemidokht; Marjan Kuchaki Rafsanjani; Brij B. Gupta; Ching-Hsien Hsu. Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems 2021, 22, 4757 -4769.
AMA StyleHamideh Fatemidokht, Marjan Kuchaki Rafsanjani, Brij B. Gupta, Ching-Hsien Hsu. Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 2021; 22 (7):4757-4769.
Chicago/Turabian StyleHamideh Fatemidokht; Marjan Kuchaki Rafsanjani; Brij B. Gupta; Ching-Hsien Hsu. 2021. "Efficient and Secure Routing Protocol Based on Artificial Intelligence Algorithms With UAV-Assisted for Vehicular Ad Hoc Networks in Intelligent Transportation Systems." IEEE Transactions on Intelligent Transportation Systems 22, no. 7: 4757-4769.
With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users’ choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user’s current context information, ignoring the impact of the user’s previous related context on the user’s current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user’s app, time, and location, into users behavior trajectories to model the users’ app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users’ contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user’s current contextual information to generate an app recommendation list that meets the user’s preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA.
Ke Zhu; Yingyuan Xiao; Wenguang Zheng; Xu Jiao; Ching-Hsien Hsu. A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories. IEEE Access 2020, 9, 1362 -1375.
AMA StyleKe Zhu, Yingyuan Xiao, Wenguang Zheng, Xu Jiao, Ching-Hsien Hsu. A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories. IEEE Access. 2020; 9 ():1362-1375.
Chicago/Turabian StyleKe Zhu; Yingyuan Xiao; Wenguang Zheng; Xu Jiao; Ching-Hsien Hsu. 2020. "A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories." IEEE Access 9, no. : 1362-1375.
Industry 4.0 is a promising evolution in the field of smart farming by improving the productivity and reducing human intervention to modernize agriculture. This smart paradigm incorporates different levels of the automation from cropping to production yield through sophisticated techniques. Different intelligent computing techniques and communication technologies are augmented with the industry paradigm for improving the efficiency of agriculture systems. This letter introduces Information Scheduling and Optimization Framework (ISOF) for optimizing the communication and information layer process in industry 4.0 architecture. Information scheduling and classification of agriculture information are optimized through this framework for reducing process latency and stagnancy. The control flexibility of a smart farm is determines using the latency and stagnancy at the end of yields. The classification part segregates information based on processing and completion time to reduce backlogs through offloading process. The advantage of this framework is that it inherits the advantages of internet of things (IoT) and edge computing (EC) technologies with interoperable feature to aid information processing, information classification, offloading, and periodic updates. The performance of the proposed framework is tested in a corn farm and some common metrics such as delayed information, processing time, audit data, and information distribution are analyzed for proving the reliability of the framework.
Gunasekaran Manogaran; Ching-Hsien Hsu; Bharat S. Rawal; Bala Anand Muthu; Constandinos X. Mavromoustakis; George Mastorakis. ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0. IEEE Internet of Things Journal 2020, 8, 3120 -3129.
AMA StyleGunasekaran Manogaran, Ching-Hsien Hsu, Bharat S. Rawal, Bala Anand Muthu, Constandinos X. Mavromoustakis, George Mastorakis. ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0. IEEE Internet of Things Journal. 2020; 8 (5):3120-3129.
Chicago/Turabian StyleGunasekaran Manogaran; Ching-Hsien Hsu; Bharat S. Rawal; Bala Anand Muthu; Constandinos X. Mavromoustakis; George Mastorakis. 2020. "ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0." IEEE Internet of Things Journal 8, no. 5: 3120-3129.
The trust information in social networks among users is an important factor for the improvement of recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. In this paper, we propose a novel trust-aware approach based on the recent advanced deep learning technique to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, \added{i.e., linear DMF and non-linear DMF} to extract better features from the user-item rating matrix for improving the initialization accuracy. The trust relationship has been integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take the neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that our proposed approaches outperforms other state-of-the-art approaches for recommendation, especially for the cold-start users.
Liangtian Wan; Feng Xia; Xiangjie Kong; Ching-Hsien Hsu; Runhe Huang; Jianhua Ma. Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks. IEEE Transactions on Network Science and Engineering 2020, 8, 511 -528.
AMA StyleLiangtian Wan, Feng Xia, Xiangjie Kong, Ching-Hsien Hsu, Runhe Huang, Jianhua Ma. Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks. IEEE Transactions on Network Science and Engineering. 2020; 8 (1):511-528.
Chicago/Turabian StyleLiangtian Wan; Feng Xia; Xiangjie Kong; Ching-Hsien Hsu; Runhe Huang; Jianhua Ma. 2020. "Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks." IEEE Transactions on Network Science and Engineering 8, no. 1: 511-528.
Victor Chang; Xiaowen Man; Qianwen Xu; Ching‐Hsien Hsu. Pairs trading on different portfolios based on machine learning. Expert Systems 2020, 1 .
AMA StyleVictor Chang, Xiaowen Man, Qianwen Xu, Ching‐Hsien Hsu. Pairs trading on different portfolios based on machine learning. Expert Systems. 2020; ():1.
Chicago/Turabian StyleVictor Chang; Xiaowen Man; Qianwen Xu; Ching‐Hsien Hsu. 2020. "Pairs trading on different portfolios based on machine learning." Expert Systems , no. : 1.
Resource allocation and management in an Internet of Things (IoT) paradigm requires precise request and response processing irrespective of its scalability support. Unpredictable traffic patterns and user density demands reliable offloading for handling user request traffic and service response. Considering the need for large-scale IoT in an account of its interoperability and heterogeneous support, this manuscript introduces a response-aware traffic offloading scheme (RTOS) for delay-sensitive user requests. This offloading scheme is supported by a multivariate spline regression machine learning model for classifying traffic for reducing the failure rate. The splines are adaptive based on the classified traffic for performing independent and shared offloading. The computation process for determining the offloading model is inherited from the cyber-physical system (CPS) coupled with the IoT-Cloud architecture. The information from the knowledge base and event logs are exploited for decision-making in employing the offloading method for the classified traffic. The simulation analysis of this scheme shows that it is effective in improving the request processing ratio and reducing processing, response time, and delay. The simulation is performed for the varying user density and traffic flows.
Gunasekaran Manogaran; Gautam Srivastava; Bala Anand Muthu; S. Baskar; P. Mohamed Shakeel; Ching-Hsien Hsu; Ali Kashif Bashir; Priyan M. Kumar. A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things. IEEE Internet of Things Journal 2020, 8, 3360 -3368.
AMA StyleGunasekaran Manogaran, Gautam Srivastava, Bala Anand Muthu, S. Baskar, P. Mohamed Shakeel, Ching-Hsien Hsu, Ali Kashif Bashir, Priyan M. Kumar. A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things. IEEE Internet of Things Journal. 2020; 8 (5):3360-3368.
Chicago/Turabian StyleGunasekaran Manogaran; Gautam Srivastava; Bala Anand Muthu; S. Baskar; P. Mohamed Shakeel; Ching-Hsien Hsu; Ali Kashif Bashir; Priyan M. Kumar. 2020. "A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things." IEEE Internet of Things Journal 8, no. 5: 3360-3368.
The Internet of Things (IoT) is the latest Internet evolution that incorporates billions of sensors, actuators, and related software services that collectively distill high value information, perform actions that affect the physical world, and support a variety of applications controlled by different organizations and individuals. IoT's ability to observe and affect the physical world presents a unprecedented opportunity for creating IoT-based smart services and products that address grant challenges in emerging opportunities in areas such as climate change, precision agriculture, smart health, advanced manufacturing, and smart cities. This special issue identifies and addresses some of the key issues that hinder the development of IoT-based solutions. It includes articles that present the latest innovations in IoT security and privacy, IoT data quality and analysis, IoT resources and task management, as well as examples of IoT-based application services and domains.
Rajiv Ranjan; Ching-Hsien Hsu; Lydia Y. Chen; Dimitrios Georgakopoulos. Holistic Technologies for Managing Internet of Things Services. IEEE Transactions on Services Computing 2020, 13, 597 -601.
AMA StyleRajiv Ranjan, Ching-Hsien Hsu, Lydia Y. Chen, Dimitrios Georgakopoulos. Holistic Technologies for Managing Internet of Things Services. IEEE Transactions on Services Computing. 2020; 13 (4):597-601.
Chicago/Turabian StyleRajiv Ranjan; Ching-Hsien Hsu; Lydia Y. Chen; Dimitrios Georgakopoulos. 2020. "Holistic Technologies for Managing Internet of Things Services." IEEE Transactions on Services Computing 13, no. 4: 597-601.
This paper provides an improved method by introducing Sentiment Analysis into the Event Study and Principal Component Analysis. The model is constructed by using the heuristic mean-end analysis. This method enables us to take into investors’ feelings towards related stocks when we study the stock market’s reaction to a given event. This paper investigates the Chinese A-shared market over 2013–2019 to study the influence of rumors and the offsetting impact of rumor clarifications on the stock price. The results indicate that no matter investor sentiment is bullish or bearish, stock price reacts significantly to rumors before as well as when the rumor goes public. Furthermore, clarification offsets the positive abnormal returns caused by rumors with bullish sentiment substantially at a limited level. Still, after five days, it creates a positive effect like the positive rumor does on the stock price. Under the bearish sentiment, clarification brings an insignificant impact on the stock price. The results indicate that the source of rumor may not come from the media and investment decisions established on rumors would be beneficial to investors before as well as after they are published. Moreover, official clarification causes an offset effect, but it is very limited.
Qianwen Xu; Victor Chang; Ching-Hsien Hsu. Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study. Information Systems Frontiers 2020, 22, 1021 -1037.
AMA StyleQianwen Xu, Victor Chang, Ching-Hsien Hsu. Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study. Information Systems Frontiers. 2020; 22 (5):1021-1037.
Chicago/Turabian StyleQianwen Xu; Victor Chang; Ching-Hsien Hsu. 2020. "Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study." Information Systems Frontiers 22, no. 5: 1021-1037.
As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investors’ sentiment varies as well. The online information that directly reflects investors’ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.
Ziyang Ji; Victor Chang; Hao Lan; Ching-Hsien Robert Hsu; Raul Valverde. Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry. Sustainability 2020, 12, 5170 .
AMA StyleZiyang Ji, Victor Chang, Hao Lan, Ching-Hsien Robert Hsu, Raul Valverde. Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry. Sustainability. 2020; 12 (12):5170.
Chicago/Turabian StyleZiyang Ji; Victor Chang; Hao Lan; Ching-Hsien Robert Hsu; Raul Valverde. 2020. "Empirical Research on the Fama-French Three-Factor Model and a Sentiment-Related Four-Factor Model in the Chinese Blockchain Industry." Sustainability 12, no. 12: 5170.