Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Databases and Information Systems (116)
- Software Engineering (80)
- Artificial Intelligence and Robotics (37)
- Engineering (37)
- Computer Engineering (35)
-
- Graphics and Human Computer Interfaces (32)
- Information Security (24)
- Digital Communications and Networking (15)
- Social and Behavioral Sciences (15)
- Theory and Algorithms (15)
- Public Affairs, Public Policy and Public Administration (11)
- Programming Languages and Compilers (10)
- Transportation (10)
- Business (8)
- Computer and Systems Architecture (8)
- Numerical Analysis and Scientific Computing (6)
- Systems Architecture (6)
- Data Storage Systems (4)
- Communication (3)
- Management Information Systems (3)
- Social Media (3)
- Technology and Innovation (3)
- Medicine and Health Sciences (2)
- Operations Research, Systems Engineering and Industrial Engineering (2)
- Public Health (2)
- Applied Mathematics (1)
- Arts and Humanities (1)
- Keyword
-
- Neural networks (12)
- Graph neural networks (11)
- Deep learning (9)
- Reinforcement learning (9)
- Task analysis (8)
-
- Training (8)
- Graph Neural Networks (7)
- Neural network (6)
- Security (6)
- Deep learning testing (5)
- Deep neural networks (5)
- Network embedding (5)
- Adaptive Resonance Theory (4)
- Categorization (4)
- Cloud computing (4)
- Clustering (4)
- Computer architecture (4)
- Finance (4)
- Meta-learning (4)
- Multimodality (4)
- Networks (4)
- Neural Networks (4)
- Neurons (4)
- Supervised learning (4)
- Uncertainty (4)
- Adaptive resonance theory (3)
- Adaptive systems (3)
- Adversarial attack (3)
- Anomaly Detection (3)
- Attention mechanisms (3)
- Publication Year
- Publication
- Publication Type
Articles 301 - 330 of 345
Full-Text Articles in OS and Networks
(Strong) Multidesignated Verifiers Signatures Secure Against Rogue Key Attack, Yunmei Zhang, Man Ho Au, Guomin Yang, Willy Susilo
(Strong) Multidesignated Verifiers Signatures Secure Against Rogue Key Attack, Yunmei Zhang, Man Ho Au, Guomin Yang, Willy Susilo
Research Collection School Of Computing and Information Systems
Designated verifier signatures (DVS) allow a signer to create a signature whose validity can only be verified by a specific entity chosen by the signer. In addition, the chosen entity, known as the designated verifier, cannot convince any body that the signature is created by the signer. Multi-designated verifiers signatures (MDVS) are a natural extension of DVS in which the signer can choose multiple designated verifiers. DVS and MDVS are useful primitives in electronic voting and contract signing. In this paper, we investigate various aspects of MDVS and make two contributions. Firstly, we revisit the notion of unforgeability under rogue …
Benchmarking Still-To-Video Face Recognition Via Partial And Local Linear Discriminant Analysis On Cox-S2v Dataset, Zhiwu Huang, S. Shan, H. Zhang, S. Lao, A. Kuerban, X. Chen
Benchmarking Still-To-Video Face Recognition Via Partial And Local Linear Discriminant Analysis On Cox-S2v Dataset, Zhiwu Huang, S. Shan, H. Zhang, S. Lao, A. Kuerban, X. Chen
Research Collection School Of Computing and Information Systems
In this paper, we explore the real-world Still-to-Video (S2V) face recognition scenario, where only very few (single, in many cases) still images per person are enrolled into the gallery while it is usually possible to capture one or multiple video clips as probe. Typical application of S2V is mug-shot based watch list screening. Generally, in this scenario, the still image(s) were collected under controlled environment, thus of high quality and resolution, in frontal view, with normal lighting and neutral expression. On the contrary, the testing video frames are of low resolution and low quality, possibly with blur, and captured under …
Defeating Sql Injection, Lwin Khin Shar, Hee Beng Kuan Tan
Defeating Sql Injection, Lwin Khin Shar, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
The best strategy for combating SQL injection, which has emerged as the most widespread website security risk, calls for integrating defensive coding practices with both vulnerability detection and runtime attack prevention methods.
Self-Organizing Neural Networks For Learning Air Combat Maneuvers, Teck-Hou Teng, Ah-Hwee Tan
Self-Organizing Neural Networks For Learning Air Combat Maneuvers, Teck-Hou Teng, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper reports on an agent-oriented approach for the modeling of adaptive doctrine-equipped computer generated force (CGF) using a commercial-grade simulation platform known as CAE STRIVECGF. A self- organizing neural network is used for the adaptive CGF to learn and generalize knowledge in an online manner during the simulation. The challenge of defining the state space and action space and the lack of domain knowledge to initialize the adaptive CGF are addressed using the doctrine used to drive the non-adaptive CGF. The doctrine contains a set of specialized knowledge for conducting 1-v-1 dogfights. The hierarchical structure and symbol representation of …
Motivated Learning For The Development Of Autonomous Agents, Janusz A. Starzyk, James T. Graham, Pawel Raif, Ah-Hwee Tan
Motivated Learning For The Development Of Autonomous Agents, Janusz A. Starzyk, James T. Graham, Pawel Raif, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically changing environments. In …
Self‐Regulating Action Exploration In Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Yuan-Sin Tan
Self‐Regulating Action Exploration In Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Yuan-Sin Tan
Research Collection School Of Computing and Information Systems
The basic tenet of a learning process is for an agent to learn for only as much and as long as it is necessary. With reinforcement learning, the learning process is divided between exploration and exploitation. Given the complexity of the problem domain and the randomness of the learning process, the exact duration of the reinforcement learning process can never be known with certainty. Using an inaccurate number of training iterations leads either to the non-convergence or the over-training of the learning agent. This work addresses such issues by proposing a technique to self-regulate the exploration rate and training duration …
Consistent Community Identification In Complex Networks, Haewoon Kwak, Young-Ho Eom, Yoonchan Choi, Hawoong Jeong
Consistent Community Identification In Complex Networks, Haewoon Kwak, Young-Ho Eom, Yoonchan Choi, Hawoong Jeong
Research Collection School Of Computing and Information Systems
We have found that known community identification algorithms produce inconsistent communities when the node ordering changes at input. We use the pairwise membership probability and consistency to quantify the level of consistency across multiple runs of an algorithm. Based on these two metrics, we address the consistency problem without compromising the modularity. The key insight of the algorithm is to use pairwise membership probabilities as link weights. It offers a new tool in the study of community structures and their evolutions.
A Hubel Wiesel Model Of Early Concept Generalization Based On Local Correlation Of Input Features, Sepideh Sadeghi, Kiruthika Ramanathan
A Hubel Wiesel Model Of Early Concept Generalization Based On Local Correlation Of Input Features, Sepideh Sadeghi, Kiruthika Ramanathan
Research Collection School Of Computing and Information Systems
Hubel Wiesel models, successful in visual processing algorithms, have only recently been used in conceptual representation. Despite the biological plausibility of a Hubel-Wiesel like architecture for conceptual memory and encouraging preliminary results, there is no implementation of how inputs at each layer of the hierarchy should be integrated for processing by a given module, based on the correlation of the features. In our paper, we propose the input integration framework - a set of operations performed on the inputs to the learning modules of the Hubel Wiesel model of conceptual memory. These operations weight the modules as being general or …
Efficient Mining Of Multiple Partial Near-Duplicate Alignments By Temporal Network, Hung-Khoon Tan, Chong-Wah Ngo, Tat-Seng Chua
Efficient Mining Of Multiple Partial Near-Duplicate Alignments By Temporal Network, Hung-Khoon Tan, Chong-Wah Ngo, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
This paper considers the mining and localization of near-duplicate segments at arbitrary positions of partial near-duplicate videos in a corpus. Temporal network is proposed to model the visual-temporal consistency between video sequence by embedding temporal constraints as directed edges in the network. Partial alignment is then achieved through network flow programming. To handle multiple alignments, we consider two properties of network structure: conciseness and divisibility, to ensure that the mining is efficient and effective. Frame-level matching is further integrated in the temporal network for alignment verification. This results in an iterative alignment-verification procedure to fine tune the localization of near-duplicate …
A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan
A Self-Organizing Approach To Episodic Memory Modeling, Wenwen Wang, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents a neural model that learns episodic traces in response to a continual stream of sensory input and feedback received from the environment. The proposed model, based on fusion Adaptive Resonance Theory (fusion ART) network, extracts key events and encodes spatiotemporal relations between events by creating cognitive nodes dynamically. The model further incorporates a novel memory search procedure, which performs parallel search of stored episodic traces continuously. Comparing with prior systems, the proposed episodic memory model presents a robust approach to encoding key events and episodes and recalling them using partial and erroneous cues. We present experimental studies, …
A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung Ku, Yangwoo Ko, Jisun An, Dongman Lee
A Social Transitivity-Based Data Dissemination Scheme For Opportunistic Networks, Jaesung Ku, Yangwoo Ko, Jisun An, Dongman Lee
Research Collection School Of Computing and Information Systems
A social-based routing protocol for opportunistic networks considers the direct delivery as forwarding metrics. By ignoring the indirect delivery through intermediate nodes, it misses chances to find paths that are better in terms of delivery ratio and time. To overcome this limitation, we propose to incorporate transitivity, which considers the indirect delivery through intermediate nodes, as one of the forwarding metrics. We also found that some message forwards do not improve the delivery performance. To reduce the number of these useless forwards, the proposed scheme forwards messages to an encountered node when the increase of total utility value is greater …
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
A Self-Organizing Neural Architecture Integrating Desire, Intention And Reinforcement Learning, Ah-Hwee Tan, Yu-Hong Feng, Yew-Soon Ong
Research Collection School Of Computing and Information Systems
This paper presents a self-organizing neural architecture that integrates the features of belief, desire, and intention (BDI) systems with reinforcement learning. Based on fusion Adaptive Resonance Theory (fusion ART), the proposed architecture provides a unified treatment for both intentional and reactive cognitive functionalities. Operating with a sense-act-learn paradigm, the low level reactive module is a fusion ART network that learns action and value policies across the sensory, motor, and feedback channels. During performance, the actions executed by the reactive module are tracked by a high level intention module (also a fusion ART network) that learns to associate sequences of actions …
Communication-Efficient Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi
Communication-Efficient Classification In P2p Networks, Hock Hee Ang, Vivekanand Gopalkrishnan, Wee Keong Ng, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Distributed classification aims to learn with accuracy comparable to that of centralized approaches but at far lesser communication and computation costs. By nature, P2P networks provide an excellent environment for performing a distributed classification task due to the high availability of shared resources, such as bandwidth, storage space, and rich computational power. However, learning in P2P networks is faced with many challenging issues; viz., scalability, peer dynamism, asynchronism and fault-tolerance. In this paper, we address these challenges by presenting CEMPaR—a communication-efficient framework based on cascading SVMs that exploits the characteristics of DHT-based lookup protocols. CEMPaR is designed to be robust …
Interference-Aware Routing Protocol In Multi-Radio Wireless Mesh Networks, Byoungheon Shin, Yangwoo Ko, Jisun An, Dongman Lee
Interference-Aware Routing Protocol In Multi-Radio Wireless Mesh Networks, Byoungheon Shin, Yangwoo Ko, Jisun An, Dongman Lee
Research Collection School Of Computing and Information Systems
Utilization of multiple radio interfaces increases throughput of wireless networks. Existing work proposes a multi-radio routing protocol exploiting link quality and channel diversity of a path. While an established path is deteriorated by interferences incurred by any changes in a network, and existing work does not detect the deterioration. In this paper, we propose an interference-aware multi-radio routing protocol detecting and resolving dynamic path deterioration in wireless mesh networks.
A Social Relation Aware Routing Protocol For Mobile Ad Hoc Networks, Jisun An, Yangwoo Ko, Dongman Lee
A Social Relation Aware Routing Protocol For Mobile Ad Hoc Networks, Jisun An, Yangwoo Ko, Dongman Lee
Research Collection School Of Computing and Information Systems
In this paper, we propose a social relation aware routing protocol for mobile ad hoc networks, which is designed for content sharing mobile social applications. Since a content can be shared by a group of users who have similar interests, the similarity of users' interests is a good metric to predict who will consume which contents. Shared interests can be exploited in routing and replication of content request and reply to achieve enhanced efficacy in content sharing. Since routing determines which content will be forwarded by whom, a route selected based on similarity of interest increases the utilization of contents …
Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan
Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper presents iFALCON, a model of BDI (beliefdesire-intention) agents that is fully realized as a selforganizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case …
A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow
A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow
Research Collection School Of Computing and Information Systems
The architecture of the human cortex is uniform and hierarchical in nature. In this paper, we build upon works on hierarchical classification systems that model the cortex to develop a neural network representation for a hierarchical spatio-temporal memory (HST-M) system. The system implements spatial and temporal processing using neural network architectures. We have tested the algorithms developed against both the MLP and the Hierarchical Temporal Memory algorithms. Our results show definite improvement over MLP and are comparable to the performance of HTM.
Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng
Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng
Research Collection School Of Computing and Information Systems
The goal of distributed learning in P2P networks is to achieve results as close as possible to those from centralized approaches. Learning models of classification in a P2P network faces several challenges like scalability, peer dynamism, asynchronism and data privacy preservation. In this paper, we study the feasibility of building SVM classifiers in a P2P network. We show how cascading SVM can be mapped to a P2P network of data propagation. Our proposed P2P SVM provides a method for constructing classifiers in P2P networks with classification accuracy comparable to centralized classifiers and better than other distributed classifiers. The proposed algorithm …
Self-Organizing Neural Models Integrating Rules And Reinforcement Learning, Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan
Self-Organizing Neural Models Integrating Rules And Reinforcement Learning, Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as fusion architecture for learning, cognition and navigation (FALCON) can incorporate a priori knowledge and perform knowledge refinement and expansion through reinforcement learning. Symbolic rules are formulated based on pre-existing know-how and inserted into FALCON as a priori knowledge. The availability of knowledge enables FALCON to start performing earlier in the initial learning trials. Through a temporal-difference (TD) learning method, the inserted rules can be refined and expanded according to the evaluative feedback signals received …
Traceable And Retrievable Identity-Based Encryption, Man Ho Au, Qiong Huang, Joseph K. Liu, Willy Susilo, Duncan S. Wong, Guomin Yang
Traceable And Retrievable Identity-Based Encryption, Man Ho Au, Qiong Huang, Joseph K. Liu, Willy Susilo, Duncan S. Wong, Guomin Yang
Research Collection School Of Computing and Information Systems
Very recently, the concept of Traceable Identity-based Encryption (IBE) scheme (or Accountable Authority Identity based Encryption scheme) was introduced in Crypto 2007. This concept enables some mechanisms to reduce the trust of a private key generator (PKG) in an IBE system. The aim of this paper is threefold. First, we discuss some subtleties in the first traceable IBE scheme in the Crypto 2007 paper. Second, we present an extension to this work by having the PKG’s master secret key retrieved automatically if more than one user secret key are released. This way, the user can produce a concrete proof of …
Integrating Temporal Difference Methods And Self‐Organizing Neural Networks For Reinforcement Learning With Delayed Evaluative Feedback, Ah-Hwee Tan, Ning Lu, Dan Xiao
Integrating Temporal Difference Methods And Self‐Organizing Neural Networks For Reinforcement Learning With Delayed Evaluative Feedback, Ah-Hwee Tan, Ning Lu, Dan Xiao
Research Collection School Of Computing and Information Systems
This paper presents a neural architecture for learning category nodes encoding mappings across multimodal patterns involving sensory inputs, actions, and rewards. By integrating adaptive resonance theory (ART) and temporal difference (TD) methods, the proposed neural model, called TD fusion architecture for learning, cognition, and navigation (TD-FALCON), enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback (reinforcement) signals. TD-FALCON learns the value functions of the state-action space estimated through on-policy and off-policy TD learning methods, specifically state-action-reward-state-action (SARSA) and Q-learning. The learned value functions are then used to determine the …
Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan
Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the …
Anonymous And Authenticated Key Exchange For Roaming Networks, Guomin Yang, Duncan S. Wong, Xiaotie Deng
Anonymous And Authenticated Key Exchange For Roaming Networks, Guomin Yang, Duncan S. Wong, Xiaotie Deng
Research Collection School Of Computing and Information Systems
User privacy is a notable security issue in wireless communications. It concerns about user identities from being exposed and user movements and whereabouts from being tracked. The concern of user privacy is particularly signified in systems which support roaming when users are able to hop across networks administered by different operators. In this paper, we propose a novel construction approach of anonymous and authenticated key exchange protocols for a roaming user and a visiting server to establish a random session key in such a way that the visiting server authenticates the user's home server without knowing exactly who the user …
Percentage-Based Hybrid Pattern Training With Neural Network Specific Cross Over, Sheng-Uei Guan, Kiruthika Ramanathan
Percentage-Based Hybrid Pattern Training With Neural Network Specific Cross Over, Sheng-Uei Guan, Kiruthika Ramanathan
Research Collection School Of Computing and Information Systems
In this paper, a new weight-setting method is proposed to improve the training time and generalization accuracy of feed-forward neural networks. This method introduces a percentage-based hybrid pattern training (PHP) scheme and aims to provide a solution to the problem dependency of other Genetic Algorithm (GA)-based Neural Network weight-setting methods. A neural network is trained using a neural network specific GA until a certain percentage of the training patterns is learned. The weights thus obtained are used as the initial weights for backpropagation (BP) training, which is then applied to complete the network training. Further improvement to the method was …
Direct Code Access In Self-Organizing Neural Networks For Reinforcement Learning, Ah-Hwee Tan
Direct Code Access In Self-Organizing Neural Networks For Reinforcement Learning, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this deficiency, this paper presents a direct code access procedure whereby TD-FALCON conducts instantaneous searches for cognitive nodes that match with the current states and at the same time provide maximal reward values. Our comparative experiments show that TD-FALCON with direct code access produces comparable performance with the original TD-FALCON while improving significantly in computation efficiency and network complexity.
Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan
Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan
Research Collection School Of Computing and Information Systems
In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …
Anonymous Dos-Resistant Access Control Protocol Using Passwords For Wireless Networks, Zhiguo Wan, Robert H. Deng, Feng Bao, Akkihebbal L. Ananda
Anonymous Dos-Resistant Access Control Protocol Using Passwords For Wireless Networks, Zhiguo Wan, Robert H. Deng, Feng Bao, Akkihebbal L. Ananda
Research Collection School Of Computing and Information Systems
Wireless networks have gained overwhelming popularity over their wired counterpart due to their great flexibility and convenience, but access control of wireless networks has been a serious problem because of the open medium. Passwords remain the most popular way for access control as well as authentication and key exchange. But existing password-based access control protocols are not satisfactory in that they do not provide DoS-resistance or anonymity. In this paper we analyze the weaknesses of an access control protocol using passwords for wireless networks in IEEE LCN 2001, and propose a different access control protocol using passwords for wireless networks. …
Security Analysis And Improvement Of Return Routability Protocol, Ying Qiu, Jianying Zhou, Robert H. Deng
Security Analysis And Improvement Of Return Routability Protocol, Ying Qiu, Jianying Zhou, Robert H. Deng
Research Collection School Of Computing and Information Systems
Mobile communication plays a more and more important role in computer networks. How to authenticate a new connecting address belonging to a said mobile node is one of the key issues in mobile networks. This paper analyzes the Return Routability (RR) protocol and proposes an improved security solution for the RR protocol without changing its architecture. With the improvement, three types of redirect attacks can be prevented.
Predictive Neural Networks For Gene Expression Data Analysis, Ah-Hwee Tan, Hong Pan
Predictive Neural Networks For Gene Expression Data Analysis, Ah-Hwee Tan, Hong Pan
Research Collection School Of Computing and Information Systems
Gene expression data generated by DNA microarray experiments have provided a vast resource for medical diagnosis and disease understanding. Most prior work in analyzing gene expression data, however, focuses on predictive performance but not so much on deriving human understandable knowledge. This paper presents a systematic approach for learning and extracting rule-based knowledge from gene expression data. A class of predictive self-organizing networks known as Adaptive Resonance Associative Map (ARAM) is used for modelling gene expression data, whose learned knowledge can be transformed into a set of symbolic IF-THEN rules for interpretation. For dimensionality reduction, we illustrate how the system …
The Value Of Mobile Applications: A Utility Company Study, Fiona Fui-Hoon Nah, Keng Siau, Hong Sheng
The Value Of Mobile Applications: A Utility Company Study, Fiona Fui-Hoon Nah, Keng Siau, Hong Sheng
Research Collection School Of Computing and Information Systems
The value proposition of mobile applications i.e. the net value of the benefits and costs associated with the adoption and adaptation of mobile applications is discussed. A means-ends network that depicts the fundamental relationships among the objectives such as efficiency, effectiveness, customer satisfaction, security, cost, and employee acceptance, was developed. The network is useful to researchers as it highlights the issues, concerns, and values of mobile applications. The network help the managers and practitioners to achieve their companies' objectives of implementing mobile and wireless applications.