Human Pose Estimation (HPE) is a core component in real-time decision systems, supporting critical applications such as healthcare monitoring, autonomous driving, and sports analytics. While deep learning models—particularly CNNs and Transformer-based architectures—have significantly improved HPE accuracy, they remain vulnerable to adversarial perturbations that subtly distort keypoint localization, thereby undermining system reliability. To address this challenge, we propose regularization and pruning transferable adversarial attack (RPA), a novel framework designed to enhance the transferability of adversarial samples in Transformer-based HPE models. RPA integrates two synergistic strategies: gradient regularization, which suppresses dominant feature correlations to reduce overfitting, and adaptive weight pruning, which removes redundant parameters to reduce model-specific noise. This dual mechanism enables the generation of transferable adversarial attacks that are effective across diverse model architectures. Extensive experiments on state-of-the-art HPE networks demonstrate that RPA consistently outperforms existing attack methods. In white-box settings, RPA reduces average precision (AP) by 0.05-0.30; in black-box scenarios, it yields AP drops of 0.01-0.04. These findings expose critical vulnerabilities in IoT-enabled HPE applications and establish a new benchmark for evaluating adversarial robustness in real-time perception systems.

Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.

Multi-access edge computing (MEC) has garnered increasing attention from users due to low web service latency. Edge servers deployed near base stations have limited resources and can only serve users within their coverage areas. Therefore, efficiently allocating users to the appropriate edge servers is crucial for significantly enhancing system performance. Traditional edge user allocation (EUA) strategies often rely on precise user location, leading to significant privacy leakage risks and weakening users' trust. To tackle this challenge, this paper presents Disguiser, a novel privacy-preserving scheme designed to achieve efficient edge user allocation while safeguarding user location privacy. Disguiser employs a Laplace noise-based location obfuscation mechanism to ensure users' privacy. To resolve the user allocation problem after location obfuscation, which involves balancing real-time performance and accuracy, we design a two-stage user allocation algorithm, TEUA, consisting of the LR-EUA initial allocation algorithm and the MR-EUA reallocation algorithm. First, a novel EUA algorithm, LR-EUA, is integrated into Disguiser, innovatively combining linear relaxation with a greedy approach. Additionally, Disguiser further minimizes resource wastage in MEC systems caused by location obfuscation by employing the MR-EUA algorithm at base stations. Experimental results show that Disguiser effectively balances privacy protection with system performance, substantially outperforming existing state-of-the-art methods.

As the Metaverse evolves, robust authentication is essential to protect digital avatar privacy from identity threats such as theft, unauthorized access, and avatar spoofing. A user’s gait, serving as an intrinsic biometric signature of their avatar, offers a seamless and continuous authentication mechanism, enhancing security. Traditional authentication methods, including passwords, biometrics, and facial or fingerprint recognition, face challenges in virtual environments due to occlusions, spoofing risks, and hardware dependencies. To address these limitations, we introduce AutoGaitAnalyzer, a novel gait authentication framework that uses 16 gait features from a large-scale simulation of 5,000 users. Benchmarked against over 10 state-of-the-art models, AutoGaitAnalyzer outperforms all, establishing a new standard for avatar security in the Metaverse.

Ensuring secure, dynamic, and fine-grained access control across independently managed domains is a major challenge in modern multi-domain environments. An access control mechanism designed for multi-domain systems needs to account for the unique characteristics of such systems, like a distributed environment, a dynamic nature, and cross-domain collaboration. This paper proposes an intelligent framework for mining Attribute-Based Access Control (ABAC) policies using a supervised learning technique for multi-domain systems. In the proposed architecture, each domain employs its own Policy Decision Engine (PDE) to evaluate access requests originating either from within the same domain or from a remote domain. For each domain, the ABAC policy is derived by training a supervised machine learning model using the access logs of that domain. Our proposed method enables the different domains to retain their autonomy by allowing them to make access decisions based on their own independent policies. We evaluate the performance of our approach on two access control datasets in terms of accuracy, precision, recall, and F1 score. Moreover, we compare our method with an existing clustering-based policy mining technique. Our proposed method significantly outperforms the existing approach in terms of the accuracy of access decision-making. This implies that our proposed framework exhibits strong generalization across domains, supporting scalable and decentralized ABAC policy learning, thereby enabling secure and interoperable access control in complex, multi-domain environments.

Effective integration of security controls is essential to safeguard organizational assets and ensure security compliance and operational resilience. In particular, organizations having DevOps often face critical challenges in the practical implementation and governance of these controls, which can significantly compromise their ability to maintain a robust security posture. In response to these challenges, a governance framework was developed to systematically address critical gaps; and guide security practitioners in integrating security controls throughout the Software Development Life Cycle (SDLC). Verifying this framework is critical to ensure its applicability and alignment with security requirements in dynamic IT environments. To achieve this, empirical validation and structured sessions were conducted with subject matter experts (SMEs) and experienced security practitioners. The sessions used real-world use cases and evaluation techniques to elicit feedback on the reliability, adaptability, and general effectiveness of the framework. This study outlines the results of the validation process and identifies potential enhancements to consider before the framework is deployed. The findings indicated that the framework is well-structured and aligned with industry best practices and security standards, such as NIST SP1and ISO 270012. In addition, all participants affirmed the comprehensiveness, traceability, and readiness of the framework for automation. It can be used effectively as a practical guide for managing security controls in both traditional and agile development environments. Ultimately, the insights gained from the feedback guided a series of refinements to improve the usability of the framework and ensure better alignment with the operational contexts of the real world.

With the explosive growth of online service platforms, an increasing number of people and enterprises are undertaking personal and professional tasks online. In real applications such as trip planning and online marketing, planning sessions for a sequence of activities or services will enable social users to receive the optimal services, improving their experience and reducing the cost of their activities. These online platforms are heterogeneous, including different types of services with different attributes. However, the problem of session planning over heterogeneous platforms has not been studied so far. In this paper, we propose a Motivation-Aware Session Planning (MASP) framework for session planning over heterogeneous social platforms. Specifically, we first propose a novel HeterBERT model to handle the heterogeneity of items at both type and attribute levels. Then, we propose to predict user preference using the motivations behind user activities. Finally, we propose an algorithm together with its optimisations for efficient session generation. The extensive tests prove the high effectiveness and efficiency of MASP.

Context: Security controls are indispensable in today’s technology-driven world for their essential role in protecting applications and systems in many organizations. They help to manage the organizational controls to ensure confidentiality, integrity and access to vital infrastructure and data (e.g., software applications, financial records, personal information, intellectual property, etc.) by ensuring that only authorized and trustworthy users have privileged access. Further, integrating security controls within the Software Development Lifecycle (SDLC) is imperative for detecting application deficiencies and preventing potential breaches that could result in financial losses and expose the systems to external and/or internal threats. They reduce the exploitation risk by identifying and patching vulnerabilities in applications and networks within the organization. Methods: To explore and get in-depth insights, a survey was conducted with 118 software practitioners to determine how they embed and handle security controls in software development environments. Our survey covers the four phases of the security controls lifecycle, including classifying, identifying, implementing, and validating security controls to understand the best practices and essential activities in each process. Results: The survey results indicated that most respondents recognized the critical importance of understanding security requirements prior to integrating appropriate security controls in each software release. We highlighted key factors that influence the selection and identification of security controls, including user group considerations, risk management practices, and organizational requirements. It appeared that security practitioners utilize a wide range of security controls that are broadly classified into six categories, where administrative and technical controls come first. With this emphasis and awareness, they could align their responses with practical and contextual factors driving effective security control implementation. Furthermore, the findings showed that most organizations rely on internal departments to implement and maintain security controls in conjunction with continuous security practices throughout the different phases of the SDLC. In contrast, only 36% of respondents utilize automated testing tools for monitoring, while 52% cite insufficient security training as a major obstacle. Conclusion: The survey highlighted the need to hire skillful security practitioners who possess a diverse range of cybersecurity skills, enabling them to govern security controls and handle troubleshooting with poise and professionalism, taking advantage of lessons learned in past experiences. The results also demonstrated the need for employing up-to-date tools and carrying out a list of best practices, to implement security controls and improve their effectiveness for the purpose of up-leveling the overall security posture. Those results emphasize the need for enhanced training programs and advanced tools to streamline security control integration. In addition, this study provides actionable insights for improving compliance and risk management, contributing to a more robust, comprehensive cybersecurity framework.

In recent years, Federated Learning (FL) has emerged as a widely adopted privacy-preserving distributed training approach, attracting significant interest from both academia and industry. Research efforts have been dedicated to improving different aspects of FL, such as algorithm improvement, resource allocation, and client selection, to enable its deployment in distributed edge networks for practical applications. One of the reasons for the poor FL model performance is due to the worker dropout during training as the FL server may be located far away from the FL workers. To address this issue, an Hierarchical Federated Learning (HFL) framework has been introduced, incorporating an additional layer of edge servers to relay communication between the FL server and workers. While the HFL framework improves the communication between the FL server and workers, large number of communication rounds may still be required for model convergence, particularly when FL workers have non-independent and identically distributed (non-IID) data. Moreover, the FL workers are assumed to fully cooperate in the FL training process, which may not always be true in practical situations. To overcome these challenges, we propose a synthetic-data-empowered HFL framework that mitigates the statistical issues arising from non-IID local datasets while also incentivizing FL worker participation. In our proposed framework, the edge servers reward the FL workers in their clusters for facilitating the FL training process. To improve the performance of the FL model given the non-IID local datasets of the FL workers, the edge servers generate and distribute synthetic datasets to FL workers within their clusters. FL workers determine which edge server to associate with, considering the computational resources required to train on both their local datasets and the synthetic datasets. The simulation results show that an evolutionary equilibrium is reached where the FL workers do not have incentive to change their edge association strategies. Given this equilibrium, the FL workers facilitate the FL training of the edge servers that they associate with and be rewarded for their contributions. The proposed framework achieves higher FL model accuracy with an addition of 5% of synthetic data.

ABSTRACT Cybersecurity has become a top priority for most organizations to protect their applications. The rapid increase in cyberattacks has necessitated a comprehensive repositioning of how security should be implemented within the software development lifecycle (SDLC). Development, Security, Operations (DevSecOps) is one of the trendy security methodologies and fastest growing development methods promoting shared responsibility for security and automating security practices at every step of the SDLC. DevSecOps is a cultural shift that integrates security controls into DevOps pipelines aiming to upscale overall security. Therefore, many organizations started to incorporate security controls within the deployment of DevSecOps through conducting continuous practices, for example, automated security testing, infrastructure as code (IaC), compliance as code, and continuous monitoring. This study aims to organize the knowledge and shed light on challenges concerning security controls during the adoption of DevSecOps, along with associated solutions and remediation workarounds reported in the literature. Further, the study aims to provide clear insights into the areas that require further investigation and research in the future. A systematic literature review (SLR) of 45 primary studies was carried out to extract data, and subsequently, the extracted data was analyzed using the thematic analysis method. This paper identifies 19 challenges related to security controls that could be experienced by security practitioners while implementing a DevSecOps model, along with 18 solutions and remediation actions suggested in literature to address and overcome some of the enlisted challenges. In addition, some gap areas are identified as opportunities for future research in this domain with the aim of improving the integration of security controls in a DevSecOps environment. Based on findings, this paper points out the importance of automation in software engineering practices, for example, continuous automation, continuous delivery, and continuous feedback, to embed security controls at the early stages of the development process.

Social video anomaly is an observation in video streams that does not conform to a common pattern of dataset's behaviour. Social video anomaly detection plays a critical role in applications from e-commerce to e-Iearning. Traditionally, anomaly detection techniques are applied to find anomalies in video broadcasting. However, they neglect the live social video streams which contain interactive talk, speech, or lecture with audience. In this paper, we propose a generic framework for effectively online detecting Anomalies Over social Video LI ve Streaming (AOVLIS). Specifically, we propose a novel deep neural network model called Coupling Long Short-Term Memory (CLSTM) that adaptively captures the history behaviours of the presenters and audience, and their mutual interactions to predict their behaviour at next time point over streams. Then we well integrate the CLSTM with a decoder layer, and propose a new reconstruction error-based scoring function REI A to calculate the anomaly score of each video segment for anomaly detection. After that, we propose a novel model update scheme that incrementally maintains CLSTM and decoder. Moreover, we design a novel upper bound and ADaptive Optimisation Strategy (ADOS) for improving the efficiency of our solution. Extensive experiments are conducted to prove the superiority of AOVLIS.

The Internet of Things (IoT) has become increasingly prevalent in various aspects of our lives, enabling billions of devices to connect and communicate seamlessly. However, the intricate nature of IoT connections and device vulnerabilities exposes the devices to security threats. To address the security challenges, we propose a proactive defense framework that leverages a model-based approach for security analysis and facilitates the defense strategies. Our proposed approach incorporates proactive defense mechanisms that combine Moving Target Defense techniques with cyber deception. The proposed approach involves the use of a decoy nodes as a deception technique and operating system based diversity as a moving target defense strategy to change the attack surface area of IoT networks. Additionally, we introduce a technique known as Important Measure-based Operating System Diversity to reduce defense cost. The effectiveness of the defense mechanisms was evaluated by using a graphical security model in a Software Defined Networking-based IoT network. Simulation results demonstrate the effectiveness of our approach in mitigating the impact of attacks while maintaining high performance levels in IoT networks.

Resilience is an increasingly important concept in current socio-economic landscapes. Due to the competitive global context and security attacks, the organisations are looking for realistic resilience assessments for operations of their digital networks. This study proposes a node Operational Resilience evaluation based on the fuzzy logic by assessing various cyber security dynamics; including node threat protection, avoiding degradation, attack identification and recovery vectors. Through extensive experiments and analysis, we reached to a better understanding of diverse relationships between cyber security factors for the evaluation of Operational Resilience.

Conventional Internet of Things (IoT) ecosystems involve data streaming from sensors, through Fog devices to a centralized Cloud server. Issues that arise include privacy concerns due to third party management of Cloud servers, single points of failure, a bottleneck in data flows and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance perspective. Blockchain technologies avoid trusted third parties and safeguard against a single point of failure and other issues. This has inspired researchers to investigate blockchain’s adoption into IoT ecosystem. In this paper, recent state-of-the-arts advances in blockchain for IoT, blockchain for Cloud IoT and blockchain for Fog IoT in the context of eHealth, smart cities, intelligent transport and other applications are analyzed. Obstacles, research gaps and potential solutions are also presented.

The identification of similar functions in malware assists analysis by supporting the exclusion of functions that have been previously analysed, allows the identification of new variants, supports authorship attribution, and the analysis of malware phylogeny. A function's context is a set comprising the function itself and all the program functions that may be executed when this function is called. Contextual features consist of data that is extracted from the functions contained in the function context. This paper presents a novel technique called Cross Version Contextual Function Similarity (CVCFS) to identify function pairs in two programs using features based on both individual functions and function context. The CVCFS technique uses Support Vector Machine (SVM) machine learning of function similarity features to pre-filter function pairs and then applies an edit distance technique using function semantics to reduce false positives. A case study is provided where individual and contextual features are extracted from three versions of Zeus malware. The SVM pre-filtering, followed by the use of an edit distance technique to filter false positives, gives a function pair identification accuracy of 85 percent.

Mobile malware detection is inherently an imbalanced data problem since the number of benign applications in the market is far greater than the number of malicious applications. Existing methods to handle imbalanced data, such as synthetic minority over-sampling, do not translate well into this domain since mobile malware detection generally deals with binary features and these methods are designed for continuous features. Also, methods adapted for categorical features cannot be applied here since random modifications of features can result in invalid sample generation. In this work, we propose a novel technique for generating synthetic samples for mobile malware detection with imbalanced data. Our proposed method adds new data points in the sample space by generating synthetic malware samples which also preserves the original functionality of the malicious apps. Experiments show that the proposed approach outperforms existing techniques in terms of precision, recall, F1score, and AUC. This study will be useful in building deep neural network-based systems to handle imbalanced data for mobile malware detection.

Recent advances in artificial intelligence, edge computing, and big data have enabled extensive reasoning capabilities at the edge of the network. Edge servers are now capable of extracting meaningful intelligence from IoT nodes, which can benefit a very diverse set of IoT applications, including smart carrier and distribution networks (power, people, water, and food), smart agriculture and manufacturing, and healthcare and maintenance. Unfortunately, as the infrastructures become more intelligent, they also become more vulnerable to disruption due to cyberattacks and information leakage. Furthermore, the rich data gathering and analytics involved in driving the intelligent management substantially raise the stakes in terms of privacy violation of the people and organizations that it serves.

Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN.

Blockchain emerging for healthcare provides a secure, decentralized and patient driven record management system. However, the storage of data generated from IoT devices in remote patient management applications requires a fast consensus mechanism. In this paper, we propose a lightweight consensus mechanism and a decentralized patient software agent to control a remote patient monitoring (RPM) system. The decentralized RPM architecture includes devices at three levels; 1) Body Area Sensor Network- medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) software replicated on the Smartphone, Fog and Cloud servers processes medical data to ensure reliable, secure and private communication. Performance analysis has been conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled remote patient monitoring system.

Cyber-attacks are becoming more sophisticated and thereby presenting increasing challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade the credibility of security services, e.g. data confidentiality, integrity, and availability. Numerous intrusion detection methods have been proposed in the literature to tackle computer security threats, which can be broadly classified into Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). This survey paper presents a taxonomy of contemporary IDS, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes. It also presents evasion techniques used by attackers to avoid detection and discusses future research challenges to counter such techniques so as to make computer systems more secure.

Decentralized content sharing (DCS) is emerging as an important platform for sharing contents among smart mobile device users, where devices form an ad-hoc network and communicate opportunistically. Existing DCS approaches for tourist spot like scenarios achieve low delivery success rate and high latency as they do not focus on dynamic demand for contents which usually vary considerably with the number of visitors present or occurrence of some influencing events. The amount of available supply also changes because of the nodes leaving the area. Only way to improve content delivery service is to distribute the contents in strategic positions based on dynamic demand and supply. In this paper, we propose a dynamic content distribution (DCD) method considering dynamic demand and supply for contents in tourist spots. Simulation results validate the improvement of the proposed approach.

Message forwarding is an integral part of the decentralized content sharing process as the content delivery success highly depends on it. Existing literature employs spatio-temporal regularity of human movement pattern and pre-existing social relationship to take message forwarding decisions. However, such approaches are ineffectual in environments where those information are unavailable such as a tourist spot or camping site. In this study, we explore the message forwarding techniques in such environments considering the information that are readily available and can be gathered on the fly. We propose a utility based forwarding scheme to select the appropriate forwarder node based on co-location stay time, connectivity and available resources. A higher co-location stay time reflects that the forwarder and the destination node is likely to have more opportunistic contacts, while the connectivity and available resource ensure that the selected forwarder has sufficient neighbours and resources to carry the message forward. Simulation results suggest that the proposed approach attains high hit and success rate and low latency for successful content delivery, which is comparable to those proposed for work-place type scenarios with regular movement pattern and pre-existing relationships.

In machine health monitoring, fault frequency identification of potential bearing faults is very important and necessary when it comes to reliable operation of a given system. In this paper, we proposed a data mining based scheme for fault frequency identification from the bearing data. In this scheme, we propose a compact tree called SAP-tree (sliding window associated frequency pattern tree) which is built upon the analysis of frequency domain characteristics of machine vibration data. Using this tree we devised a sliding window-based associated frequency pattern mining technique, called SAP algorithm, that mines for the frequencies relevant to machine fault. Our SAP algorithm can mine associated frequency patterns in the current window with frequent pattern (FP)-growth like pattern-growth method and used these patterns to identify the fault frequency. Extensive experimental analyses show that our technique is very efficient in identifying fault frequency over vibration data stream.

Smart mobile devices have become immensely popular among the people worldwide and provide a new platform for generating and sharing contents. The centralized and hybrid architectures for content sharing require constant Internet connection, increase traffic and incur costs. To address these issues several content sharing approaches have been proposed using the decentralized architecture. Most of the proposed approaches uses patio-temporal regularity and pre-existing social relationships of the users to predict their movements and facilitate content sharing. However, there are scenarios such as visiting hotspots where regular movement patterns or established social relationships among people might not exist. Content sharing in such scenarios has not been addressed yet in literature and existing prediction based approaches are ineffectual. This study focuses on facilitating content sharing in the afore-mentioned scenarios. We take account of user interests, recommendations from online social networks, hotspot specific activities and other relevant information to construct communities which facilitate content sharing. For each community an administrator, who maintains content and member lists and render directory services, is selected based on stay probability, interest score, battery lifetime and device configuration. Simulation results show that our proposed approach attains high content hit and success rate and low latency in delivery which is nearly comparable to those proposed for scenarios with regular predictable movement patterns reported in literature.

Mining of sensor data for useful knowledge extraction is a very challenging task. Existing works generate sensor association rules using occurrence frequency of patterns to extract the knowledge. These techniques often generate huge number of rules, most of which are non-informative or fail to reflect true correlation among sensor data. In this paper, we propose a new type of behavioral pattern called associated sensor patterns which capture association-like co-occurrences as well as temporal correlations which are linked with such co-occurrences. To capture such patterns a compact tree structure, called associated sensor pattern tree (ASP-tree) and a mining algorithm (ASP) are proposed which use pattern growth-based approach to generate all associated patterns with only one scan over dataset. Moreover, when data stream flows through, old information may lose significance for the current time. To capture significance of recent data, ASP-tree is further enhanced to SWASP-tree by adopting sliding observation window and updating the tree structure accordingly. Finally, window size is made dynamically adaptive to ensure efficient resource usage. Different characteristics of the proposed techniques and their computational complexity are presented. Experimental results show that our approach is very efficient in discovering associated sensor patterns and outperforms existing techniques.

Mining interesting knowledge from the huge amount of data gathered from WSNs is a challenge. Works reported in literature use support metric-based sensor association rules which employ the occurrence frequency of patterns as criteria. However, consideration of the binary frequency of a pattern is not a sufficient indicator for finding meaningful patterns because it only reflects the number of epochs which contain that pattern in the dataset. The share measure of sensorsets could discover useful knowledge about trigger values associated with a sensor. Here, we propose a new type of behavioral pattern called share-frequent sensor patterns (SFSPs) by considering the non-binary frequency values of sensors in epochs. SFSPs can find a correlation among a set of sensors and hence can improve the performance of WSNs in a resource management process. In this paper, a share-frequent sensor pattern tree (ShrFSP-tree) has been proposed to facilitate a pattern growth mining technique to discover SFSPs from WSN data. We also present a parallel and distributed method where the ShrFSP-tree is enhanced into PShrFSP-tree and its performance is investigated for both homogeneous and heterogeneous systems. Results show that our method is time and memory efficient in finding SFSPs than the existing most efficient algorithms.

Due to recent advances in wireless sensor networks (WSNs) and their ability to generate huge amount of data in the form of streams, knowledge discovery techniques have received a great deal of attention to extract useful knowledge regarding the underlying network. Traditionally sensor association rules measure occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. In this paper, we propose a new type of sensor behavioral pattern called associated sensor patterns that captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. We also propose a novel tree structure called as associated sensor pattern tree (ASPT) and a mining algorithm, associated sensor pattern (ASP) which facilitates frequent pattern (FP) growth-based technique to generate all associated sensor patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our algorithm is very efficient in finding associated sensor patterns than the existing significant algorithms.

WSNs generates a large amount of data in the form of stream and mining knowledge from the stream of data can be extremely useful. Association rules mining, from the sensor data, has been studied in recent literature. However, sensor association rules mining often produces a huge number of rules, but most of them either are redundant or fail to reflect the true correlation relationship among data objects. In this paper, we address this problem and propose mining of a new type of sensor behavioral pattern called associated-correlated sensor patterns. The proposed behavioral patterns capture not only association-like co-occurrences but also the substantial temporal correlations implied by such co-occurrences in the sensor data. Here, we also use a prefix tree-based structure called associated-correlated sensor pattern-tree (ACSP-tree), which facilitates frequent pattern (FP) growth-based mining technique to generate all associated-correlated patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our approach is time and memory efficient in finding associated-correlated patterns than the existing most efficient algorithms.

View-invariant action recognition is one of the most challenging problems in computer vision. Various representations are being devised for matching actions across different viewpoints to achieve view invariance. In this paper, we explore the invariance property of temporal order of action instances during action execution and utilize it for devising a new view-invariant action recognition approach. To ensure temporal order during matching, we utilize spatiotemporal features, feature fusion and temporal order consistency constraint. We start by extracting spatiotemporal cuboid features from video sequences and applying feature fusion to encapsulate within-class similarity for the same viewpoints. For each action class, we construct a feature fusion table to facilitate feature matching across different views. An action matching score is then calculated based on global temporal order constraint and number of matching features. Finally, the action label of the class with the maximum value of the matching score is assigned to the query action. Experimentation is performed on multiple view Inria Xmas motion acquisition sequences and West Virginia University action datasets, with encouraging results, that are comparable to the existing view-invariant action recognition techniques.

Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available data sets, WATF achieves higher fault detection accuracy, even when training and testing data sets belong to different severity levels.

Vibration signals, used for abnormality detection in machine health monitoring (MHM), exhibit significant variation with varying fault severity. This signal variation causes overlap among the features characterizing different types of faults, which results in severe performance degradation of the fault diagnostic model. In this paper, a wavelet based adaptive training set and feature selection (WATF) self-configuration scheme is presented, which selects the optimum wavelet decomposition level, and employs adaptive selection of the training set and features. Optimal wavelet decomposition level selection is such that the maximum fault signature-signal energy bands are achieved. The severity variant features, which could cause detrimental class overlap for MHM, are avoided using adaptive selection of the training set and features based on the location of a test data in feature space. WATF uses Support Vector Machines (SVM) to build the fault diagnostic model, and its performance and robustness has been tested with data having different severity levels. Comparative studies of WATF with eight existing fault diagnosis schemes show that, for publicly available datasets, WATF achieves higher fault detection accuracy, even when training and testing datasets belong to different severity levels.

Multiple-point defects and abraded surfaces in rotary machinery induce complex vibration signatures, and have a tendency to mislead defect diagnosis models. A challenging problem in machine defect diagnosis is to model and study defect signature dynamics in the case of multiple-point defects and surface abrasion. In this study, a multiple-point defect model (MPDM) that characterizes the dynamics of n-point bearing defects is proposed. MPDM is further extended to model degradation in a rotating machine as a special case of multiple-point defects. Analytical and experimental results for multiple-point defects and abrasions show that the location of the fundamental defect frequency shifts depending upon the relative location of the defects and width of the abrasive region. This variation in the defect frequency results in a degradation of the defect detection accuracy of the defect diagnostic model. Based on envelope detection analysis, a modification in existing defect diagnostic models is recommended to nullify the impact of multiple-point defects, and general abrasion in machine components.

Complex target tracking applications require active sensor nodes to collaboratively track multiple moving targets, which can balance the trade-off between the quality of tracking and network's lifetime. In this paper, we develop a distributed sensor-selection protocol (DSSP) to activate dynamic number of sensors based on the cost metrics. Cost metrics contains energy-aware leadership cost and eagerness-based tracking cost; which selects sensors with higher energy resources and information utilities. DSSP enables an even distribution of energy consumption among the nodes to prolong the network lifetime. Our results show that the proposed scheme can significantly improve the network lifetime while maintaining the high tracking accuracy as compared to the other schemes.

Efficient target tracking applications use active sensor nodes collaboratively to track multiple moving targets by balancing the trade-off between the quality of tracking and network's lifetime. In this paper, we propose a low-energy dynamic sensor selection (LEDS) scheme to track multiple targets by estimating energy consumption of sensors and information utility projection of the targets on sensors to calculate the eagerness in tracking. Eagerness represents the eligibility of a sensor node to be selected for tracking, considering relative profiles of other sensors and location of all the targets in its vicinity. LEDS enables an even distribution of energy consumption among the nodes to prolong their remaining energies. Our results show that the proposed scheme can significantly improve the network lifetime over the existing methods while maintaining the high tracking accuracy in congested areas where multiple concurrent targets overlap.

Efficient target tracking applications require active sensor nodes to track a cluster of moving targets. Clustering could lead to significant cost improvement as compared to tracking individual targets. This paper presents accurate clustering of targets for both coherent and incoherent movement patterns. We propose a novel clustering algorithm that utilises an implicit dynamic time frame to assess the relational history of targets in creating a weighted graph of connected components. The proposed algorithm employs key features of localisation algorithms in target tracking, namely, estimated current and predicted locations to determine the relational directions and distances of moving targets. Our simulation results show a significant improvement on the clustering accuracy and computation time by dynamically adjusting the history-window size and predicting the relationships among targets.

In a WLAN-cellular integrated network scenario, the most important point of consideration is the vertical handoff strategy applied for WLAN admission control. The admission control has previously been based on the parameters like congestion level in WLAN network. We propose a novel approach for handoff to WLAN by using connectivity graph data from online social networking services. Stronger social ties with other users advocate a higher probability of admission to WLAN. The main benefit of proposed handover strategy is diminution in global social network traffic. Simulation results prove the validity of our proposed approach against conventional methods.

Energy efficiency is a vital aspect of resource constrained wireless sensor networks (WSNs). All protocols designed for WSNs must be energy aware in order to prolong the network lifetime. In this paper, we have designed a novel MAC layer protocol (I-MAC: Intelligent MAC) for WSNs. By exercising intelligent sleep and wake-up schedule, I-MAC saves energy of the resource constrained sensor nodes greatly. At the same time, I-MAC does not compromise its operational performances. Both analytical study and simulation prove that I-MAC is not only highly energy efficient but also its operational performances are better than similar protocols.

Wireless sensor networks (WSNs), now-a-days, are deployed in environmental data collection as well as in critical event monitoring. Successful data collection requires reliability while reliable event detection necessitates timeliness. Simultaneous data gathering and event monitoring is not well studied in literature. In this paper, we propose a system model that works on homogeneous data gathering WSNs. When an event occurs, an event cluster with a different transmission channel is formed and both data gathering and event monitoring are performed at the same time. The proposed model has a novel routing strategy with a built-in congestion control technique to provide timely delivery of event data. Experimental results show that the proposed method performs better than known similar techniques in terms of reliable data gathering and reliable timely event monitoring. It also enhances the network lifetime significantly compared to other existing methods.

Variation in viewpoints poses significant challenges to action recognition. One popular way of encoding view-invariant action representation is based on the exploitation of epipolar geometry between different views of the same action. Majority of representative work considers detection of landmark points and their tracking by assuming that motion trajectories for all landmark points on human body are available throughout the course of an action. Unfortunately, due to occlusion and noise, detection and tracking of these landmarks is not always robust. To facilitate it, some of the work assumes that such trajectories are manually marked which is a clear drawback and lacks automation introduced by computer vision. In this paper, we address this problem by proposing view invariant action matching score based on epipolar geometry between actor silhouettes, without tracking and explicit point correspondences. In addition, we explore multi-body epipolar constraint which facilitates to work on original action volumes without any pre-processing. We show that multi-body fundamental matrix captures the geometry of dynamic action scenes and helps devising an action matching score across different views without any prior segmentation of actors. Extensive experimentation on challenging view invariant action datasets shows that our approach not only removes long standing assumptions but also achieves significant improvement in recognition accuracy and retrieval.

Wireless access technology has come a long way in its relatively short but remarkable lifetime, which has so far been led by the WiFi technology. While WiFi enjoys a high penetration in the market, its hotspots are connected to the internet through wired connections, making its deployment cost very high. WiMAX has emerged as an existing new wireless technology, which provides larger coverage and higher bandwidth. Deployment of WiMAX only infrastructure, however, is highly expensive, which has motivated researchers to search for a low cost integrated WiMAX/WiFi architecture (using WiMAX as the backhaul and WiFi as the last mile technology) that supports 4G applications and provides high speed broadband services. WiMAX technology is equipped with mechanisms capable of delivering guaranteed quality of service (QoS). WiFi, on the other hand, has very limited capacity for providing QoS to the end applications. Delivering improved QoS in an integrated WiMAX/WiFi architecture poses a serious technological challenge. In this paper, we depict a converged architecture of WiMAX and WiFi, and then propose an adaptive resource distribution model for the access points. The new model is designed as an optimization problem that maximizes the QoS utility of the network. A new QoS utility function is proposed that takes the connection priority and continuity into account. Our simulation results show that our proposed scheme maintains QoS in different scenarios whereas existing other resource sharing schemes experience violation of QoS (minimum rate requirement) in 66% cases.

Optimum selection of sensors in target tracking applications has a great potential to maintain right trade-off between energy consumption and quality of tracking. In this paper, we propose a dynamic sensor selection scheme to achieve energy efficiency while ensuring the required quality of tracking. To this end, relative information utility projection of a target on sensors' observation is used in niche overlap measurements. Niche overlap measures are used to assess the similarity in information utilities where information utility is inversely proportional to error in target's state estimation based on prior distribution. The proposed scheme is a greedy approach in which sensor nodes are selected such that the overall niche overlap of all the selected nodes is maximized until the required level of accuracy is achieved. Our simulation results show significant improvement in tracking accuracy and network's lifetime over the existing methods.

Cellular networks coupled with wireless local area networks would be very common in the next- generation wireless environments. This integration would be made possible by employing techniques of vertical handover across different radio access networks. Finding precise timing to carry out vertical handover is an important problem and is the topic of this research work. We propose a hybrid vertical handoff (HVH) initiation by intelligent fusion of adaptive threshold, signal trend detection and variable width dwell timer. Simulation studies have shown that our proposed approach results in a better vertical handoff experience as compared to existing techniques.

Wireless local area networks (WLANs) offer a promising role in the fourth-generation heterogeneous wireless networks. This requires efficient and timely switching of a mobile node's connection (called vertical handover) from cellular network to WLAN. Existing methods to initiate vertical handover do not fully utilize the WLAN potential and result in switching to cellular networks even when a WLAN network is available. We propose a hybrid approach to determine vertical handover timing with a goal to maximize the utilization of WLAN resources, while maintaining a low probability of handover failure. Simulation results indicate the proposed technique showing better performance in terms of number of ping-pong events and handover dropping probability as compared to existing techniques which are based on mean RSS, FFT and adaptive threshold.

Increase in the number of coexisting networks in license free Industrial, Scientific and Medical (ISM) band causes interferences for industrial automation, e.g., shop floors of manufacturing facilities. In order to ensure the reliability for automation networks, interference avoidance schemes are required. This paper proposes a novel Predefined Hopping Pattern (PHP) technique for frequency hopping in ISM band, which mitigates self-interferences and static interferers as well. This technique generates optimized frequency hopping sequences which ensure sufficient frequency diversity and frequency offset among the coexisting Bluetooth piconets and exploits transmission experiences for a particular frequency in eliminating interference. Simulation studies have shown that PHP has better collision avoidance rate than well known adaptive frequency hopping (AFH) and adaptive frequency rolling (AFR) schemes.

Increase in the number of coexisting networks in Industrial, Scientific and Medical (ISM) band cause interferences and demands for intelligent interference avoidance schemes. This paper proposes a novel Diversified Adaptive Frequency Rolling (DAFR) technique for frequency hopping in Bluetooth piconets which has the tendency to mitigate both the self and static interferences and ensures sufficient frequency diversity. Simulation studies validate the prospects for the proposed scheme to be used for frequency hopping networks against already existing techniques, Adaptive Frequency Hopping (AFH) and Adaptive Frequency Rolling (AFR).

Color image fusion of visible and infra-red imagery can play an important role in multi-sensor night vision systems that are an integral part of modern warfare. Image fusion minimizes the amount of required bandwidth by transmitting the fused image rather than multiple sensor images. Color image fusion can be achieved by combining inputs from original colored sensors or by employing pseudo colorization and color transfer to grayscale images. Various quality measures have been proposed for multi-sensor grayscale image fusion techniques; but no appropriate quality measure has been devised for the quality evaluation of multi-sensor color image fusion. In this paper, we propose a novel color image fusion quality measure, Color Fusion Objective Index (CFOI) based on colorfulness, gradient similarity and mutual information techniques. Experimental results show the effectiveness of CFOI to evaluate the color and salient feature extraction introduced by color fusion techniques into the final fused imagery as well as its consistency with subjective evaluation.

In this paper, we present an automated color transfer based video fusion method to attain real-time color night vision capability for night-time video surveillance. We utilize simple RGB Color transfer technique to fused pseudo colored video frames without conversion to any uncorrelated color space. We investigated that final color fusion results greatly depend on the selection of target color Image. Therefore, rather than using any arbitrary target color image based on mere general visual anticipation, we have automated target color image selection using structural similarity and color saturation. We further apply color enhancement to improve final appearance of color fused images. Subjective and objective quality evaluations greatly indicate the effectiveness of our color video fusion method for nighttime video surveillance applications.

Successful event detection in Wireless Sensor Networks (WSN) requires reliability and timeliness. When an event occurs, the base station (BS) is particularly interested about reliable and timely collection of data sent by the nodes close to the event, and the data sent by other nodes have little importance. In this paper, we propose Congestion Avoidance and Mitigation (CAM) protocol that performs the function of a routing protocol as well as the function of a transport protocol. During routing, it avoids congestion by avoiding congested routes. It also mitigates congestion by utilizing an accurate data-rate adjustment when congestion occurs. Experimental results show that CAM is capable of avoiding and mitigating congestion effectively, and performs better than similar known techniques in terms of reliable and timely event detection.

Trade-off between energy-efficiency and reliability in wireless sensor networks is application dependent. Without the reliability, the extended lifetime of a network is of limited use. Due to the inherent correlation between reliability and congestion, it is necessary to reduce congestion to improve reliability. Existing congestion control algorithms in wireless sensor networks are reactive. They attempt to reduce the congestion only after its detection. In this paper, we present Location Aided Congestion Aware Routing (LACAR) protocol that proactively avoids congestion formation and improves data delivery success rate in data gathering wireless sensor networks. Location, energy and congestion information of neighbours together with the location information of the base station determine appropriate routes. Simulation results show that LACAR achieves high packet success rate in an energy-efficient way.

The Suburban Ad Hoc Network (SAHN) is a cooperative ad hoc wireless mesh network. Nodes are owned and operated by end-users without reliance on central infrastructure. It provides symmetrical bandwidth allowing peer-to-peer services and distributed servers. We minimize the use of scarce unlicensed RF spectrum supported by Smart Antenna technology. RF interference in such networks and techniques and strategies to reduce it are examined. Traffic is spread across multiple frequency channels, and multiple directional beams to achieve improved spatial re-use. We focus on the control of smart antennas rather than their design. By dynamically adjusting our network topology using Smart Antennas and dynamically re-routing current communications we optimize the network for its current traffic needs.

Wireless ad hoc networks are self-configurable distributed systems. One of the major problems in traditional wireless ad hoc networks is interference. The interference could be reduced using smart directional antennas. In this study, multibeam smart antennas have been used. When using this type of antenna, two nodes can communicate when both the sending and receiving beams are pointing towards each other. Also, a node can only communicate with a subset of nodes in its neighborhood depending on the number of beams and their beamwidth. Thus, the network topology needs to be dynamic in this case, andby controlling the topology network, performance can be increased. In this paper, we present a framework of a cross layer approach of topology control that interacts with the Routing layer and MAC layer and meets the required QoS of different data streams. The approach is fully distributed. When the network is initialized, the algorithm builds an initial connected topology and the routing algorithm uses this topology to find paths for the current communications. Then, depending on the network scenario, current communications and the required QoS, the topology control layer changes the topology to optimize the network performance. This study concerns Suburban Ad Hoc Networks (SAHN) where nodes tend to be fixed and are aware of their locations.

Soft handoff in WCDMA systems allows multi connection between the user and base stations during handoff, in contrast to single connection in hard handoff. But multi connection flexibility leads to a trade-off between quality of service for the user and the system downlink capacity. The heterogeneous wireless networks consist of WCDMA and WLAN systems, which operate at different frequency with no direct interference. Therefore, a vertical soft handoff between downlink shared channels from WCDMA and WLAN will not suffer similar side effects as the horizontal soft handoff in WCDMA systems. In this paper, we present an analytical framework for vertical soft handoff and propose a context-aware vertical soft handoff algorithm (CAVSH) for heterogeneous wireless networks. CAVSH considers four user and system context parameters such as user required bandwidth, user traffic cost, access network utilization, and signal to interference-and-noise ratio (SINR). The results show that the proposed CAVSH can provide the system with lower dropping probability, lower average cost to the user and higher throughput, as compared with vertical hard handoff.

Next generation heterogeneous wireless networks offer the end users with assurance of QoS inside each access network as well as during vertical handoff between them. For guaranteed QoS, the vertical handoff algorithm must be QoS aware, which cannot be achieved with the use of traditional RSS as the vertical handoff criteria. In this paper, we propose a novel vertical handoff algorithm which uses received SINR from various access networks as the handoff criteria. This algorithm consider the combined effects of SINR from different access networks with SINR value from one network being converted to equivalent SINR value to the target network, so the handoff algorithm can have the knowledge of achievable bandwidths from both access networks to make handoff decisions with QoS consideration. Analytical results confirm that the new SINR based vertical handoff algorithm can consistently offer the end user with maximum available bandwidth during vertical handoff contrary to the RSS based vertical handoff, whose performance differs under different network conditions. System level simulations also reveal the improvement of overall system throughputs using SINR based vertical handoff, comparing with the RSS based vertical handoff.

This paper proposes a cross-layer protocol for energy-aware routing in wireless sensor networks. The protocol combines the energy depreciation rate, node distance and neighbourhood information from physical layer together with TDMA schedules from the MAC layer and also network life requirements from the application layer, to effectively determine the most efficient routes to the base station. This cross layer efficiency measure is then used to form dynamic clusters that adapt to changing traffic and energy conditions so assisting to both control the transmission power and schedule the sleep-wake cycles of nodes for better energy utilisation. The proposed protocol is a recursive aggregation scheme that transforms a network-wide routing dissemination problem into a single-hop query protocol that aids nodes in making multi-hop routing decisions based solely upon the information provided by their single-hop neighbours. Results confirm that the proposed technique balances the load on forwarding nodes, adapts the MAC layer precisely to the routing layer and minimizes data delivery time for increased traffic and large scale networks.

Any ad hoc ubiquitous network should be capable of taking proactive measures for smooth reconfiguration in the event of energy loss and a failure to key network Parent Nodes (PN). The overall objective of reconfiguration is to keep the network operating effectively for a prescribed time frame. The load profiles of PNs can be used to define its current state as well as to predict potential failures caused by energy loss due to high loads on particular PNs. This paper presents a new technique utilising historical load profile information about each PN to improve the estimate accuracy of its remaining lifetime before failure. A novel load balancing model is introduced that maintains PNs in a state whereby the time- to-live requirement is met by the PNs comprising the backbone of the network. Results prove that the proposed methodologies achieve the reconfiguration objectives as well as maintaining QoS, especially in high density ad hoc networks.

Most of gene regulatory network (GRN) studies are based on crisp and parametric algorithms, despite inherent fuzzy nature of gene co-regulation. This paper presents adaptive fuzzy evolutionary GRN Reconstruction (AFEGRN) framework for modeling GRNs. The AFEGRN automatically determines model parameters, such as, number of clusters for fuzzy c-means using fuzzy-PBM index and estimation of Gaussian distribution algorithm. The proposed strategy was tested for breast cancer and normal GRNs. The results conformed to biological knowledge and showed that most of cancer related GRN changes were caused by differentially expressed genes. This demonstrates effectiveness of AFEGRN to model any GRN.

Genetic microarray expression data often contains multiple missing values that can significantly affect the performance of statistical and machine learning algorithms. This paper presents an innovative missing value estimation technique, called collateral missing value estimation (CMVE) which has demonstrated superior estimation performance compared with the K-nearest neighbour (KNN) imputation algorithm, the least square impute (LSImpute) and Bayesian principal component analysis (BPCA) techniques. Experimental results confirm that CMVE provides an improvement of 89%, 12% and 10% for the BRCA1, BRCA2 and sporadic ovarian cancer mutations, respectively, compared to the average error rate of KNN, LSImpute and BPCA imputation methods, over a range of randomly selected missing values. The underlying theory behind CMVE also means that it is not restricted to bioinformatics data, but can be successfully applied to any correlated data set.

Proactive self-configuration is indispensable for MANETs like ubiquitous sensor networks (USNs), as component devices of the network are usually exposed to natural or man-made disasters due to the hostile deployment and ad hoc nature of USNs. Network state beacons (NSBs) are exchanged among the key nodes of the network for crucial and effective monitoring of the network for steady state operation. The rate of beacon exchange (F/sub E/) and its contents, define the time and nature of the proactive action. Therefore it is very important to optimize these parameters to tune the functional response of the USN. The paper presents a novel F/sub E/ selection model based on autonomic, load-adaptive optimization of beacon exchange rate for monitoring and proactively reconfiguring the network. The results confirm the improved throughput while maintaining QoS with a little overhead control traffic.

MOTIVATION: Microarray data are used in a range of application areas in biology, although often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible before using these algorithms. While many imputation algorithms have been proposed, more robust techniques need to be developed so that further analysis of biological data can be accurately undertaken. In this paper, an innovative missing value imputation algorithm called collateral missing value estimation (CMVE) is presented which uses multiple covariance-based imputation matrices for the final prediction of missing values. The matrices are computed and optimized using least square regression and linear programming methods. RESULTS: The new CMVE algorithm has been compared with existing estimation techniques including Bayesian principal component analysis imputation (BPCA), least square impute (LSImpute) and K-nearest neighbour (KNN). All these methods were rigorously tested to estimate missing values in three separate non-time series (ovarian cancer based) and one time series (yeast sporulation) dataset. Each method was quantitatively analyzed using the normalized root mean square (NRMS) error measure, covering a wide range of randomly introduced missing value probabilities from 0.01 to 0.2. Experiments were also undertaken on the yeast dataset, which comprised 1.7% actual missing values, to test the hypothesis that CMVE performed better not only for randomly occurring but also for a real distribution of missing values. The results confirmed that CMVE consistently demonstrated superior and robust estimation capability of missing values compared with other methods for both series types of data, for the same order of computational complexity. A concise theoretical framework has also been formulated to validate the improved performance of the CMVE algorithm. AVAILABILITY: The CMVE software is available upon request from the authors.

An optimal genetic mutation diagnosis requires proper selection of mutation classifier. This work investigates the performance of different classification, missing value estimation (MVE) and data dimension reduction techniques for the classification of gene expression data for BRCA1, BRCA2 and Sporadic mutations of epithelial ovarian cancer. Bayesian MVE and zero imputation techniques were employed to deal with missing values. Our study showed the better performance of the Bayesian technique. A novel approach is introduced to use generalized regression neural network (GRNN) as genetic mutation classifier which completely outperformed both well established support vector machine and probabilistic neural network.

Correct classification and prediction of tumor cells is essential for successful diagnosis and reliable future treatment. However, it is very challenging to distinguish between tumor classes using microarray with thousands of gene expressions. Removing irrelevant genes is very helpful for us to learn the relationship between genes and tumors. In this paper we have used two methods: multivariate permutation test (MPT) and significant analysis of microarray (SAM) to select significant genes for feature selection. Using those selected features, we applied support vector machine, (SVM) with polynomial, radial and linear kernels, to predict the class of testing data. Our result shows that all the samples are classified correctly. We have achieved 100% accuracy in classification among all the samples with polynomial kernel of SVM while Liner kernel shows no misclassification among BRCA1-BRCA2 and BRCA1-sporadic.

This paper presents decision-based fusion models to classify BRCA1, BRCA2 and Sporadic genetic mutations for breast and ovarian cancer. Different ensembles of base classifiers using the stacked generalization technique have been proposed including support vector machines (SVM) with linear, polynomial and radial base function kernels. A generalized regression neural network (GRNN) is then applied to predict the mutation type based on the outputs of base classifiers, and experimental results show that the new proposed fusion methodology for selecting the best and removing weak classifiers outperforms single classification models.

Microarray data often contains multiple missing genetic expression values that degrade the performance of statistical and machine learning algorithms. This paper presents a K ranked diagonal covariance-based missing value estimation algorithm (KRCOV) that has demonstrated significantly superior performance compared to the more commonly used K-nearest neighbour (KNN) imputation algorithm when it is applied to estimate missing values of BRCA1, BRCA2 and Sporadic genetic mutation samples present in ovarian cancer. Experimental results confirm KRCOV outperformed both KNN and zero imputation techniques in terms of their classification accuracies when used toimpute randomly missing values from 1% to 5%.The classifier used for this purpose was the Generalized Regression Neural Network.The paper also provides a hypothesis for why KRCOV performs better than KNN not only for bioinformatics data but also for other data types having strong correlated values.