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As a component of Wireless Sensor Network (WSN), Visual-WSN (VWSN) utilizes cameras to obtain relevant data including visual recordings and static images. Data from the camera is sent to energy efficient sink to extract key-information out of it. VWSN applications range from health care monitoring to military surveillance. In a network with VWSN, there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy, memory and I/O resources. In this case, Mobile Sinks (MS) can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head (CH), it also collects data from nearby nodes as well. The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system. However, making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account. We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe, learn and understand things from manual perspective. Proposed architecture is designed based on Mamdani's fuzzy model. Following parameters are derived based on the model residual energy, node centrality, distance between the sink and current position, node centrality, node density, node history, and mobility of sink as input variables for decision making in CH selection. The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN. The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm (GA) and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules. Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system. Simulation results are obtained using MATLAB. The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACHExpected Residual Energy (LEACH-ERE). [ABSTRACT FROM AUTHOR]

Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property. The precise classification of cyclone tracks is significantly important in the field of weather forecasting. In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape. Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the TC characteristics. A set of SWRL (Semantic Web Rule Language) rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology. Furthermore, we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset. According to experiments on the IBTrACS dataset, the proposed ontology based SVM classifier achieves an accuracy of 98.3% with reduced classification error rates. [ABSTRACT FROM AUTHOR]

SIM card cloning is a cyber-attack that is increasing dramatically in number. The Subscriber Identity Module (SIM) holds the crucial user data required to establish a connection between several telecom companies. Concerns about user security and privacy are growing as the usage of smartphones and the Internet has increased worldwide in the technology industry. Therefore, it is imperative for all users to understand cybercrime and security. This study analyzed the emerging trends and challenges associated with these threats, highlighting real-world risks and repercussions through case studies. As mobile devices continue to play an integral role in our lives, they have become lucrative targets for cybercriminals seeking unauthorized access to personal and corporate data. Cybercrime that uses telecom technology to steal information and exploit victims, known as phishing is discussed. Innovative security solutions and best practices that aim to counteract SIM card vulnerabilities, protect user identities, and safeguard sensitive information are also discussed. By examining legal frameworks, preventive measures, and technological advancements, this study serves as a valuable resource for security professionals, policymakers, and individuals seeking to fortify mobile security defenses. [ABSTRACT FROM AUTHOR]

Though cloud computing has grabbed the attention of several industrialists and educationalists, the only concern of cloud computing is the uncontrollable rise of cloud data centres. The improper utilization of cloud resources paves way for inefficiency and environmental hazard as well. Understanding the seriousness of this issue, several researchers contribute to promote green cloud computing through different ways. The Green Cloud Computing is the act of executing approaches and the techniques to improve proficiency of the figuring assets so as to decrease the vitality utilization and natural effect of their usage. The power consumption of the data centre offers the features like web based checking, live virtual machine movement, and advancement of Virtual Machine Placement. This work focuses on effective resource allocation scheme for cloud users, which does not compromise on QoS by employing two layers such as Cloud Manager Layer (CMLs) and Green Manager Layer (GML). The CML is responsible for choosing the suitable resources out of all available resources and the GML picks the best one out of it. Due to this optimal selection of resource, the average service response time is reduced at the cost of minimized power consumption. When handling 500 service requests, the proposed work consumes about 4298 W and the comparative approaches consume more power. [ABSTRACT FROM AUTHOR]

Cochlear implantation is a surgical procedure by which an electronic medical device, namely a cochlear implant, is fitted to the individual who has the challenge of hearing. It takes the function of the damaged inner ear, the cochlea. The hearing impaired may not benefit from this procedure because of cross-modal plasticity. This work aims to study the implantee's visual modal adaptation by analyzing the visual evoked potential. The proposed methodology analyses the existence of cross-modal reorganization in the auditory cortex of bilateral prelingually deaf children after cochlear implantation using visual evoked potential. Fifty healthy, 50 prelingually, deaf children 50 cochlear implanted were considered as a cohort of the Visual evoked potential. The evoked potential recorded using pattern reversal stimulation. The amplitude and latency of N75, P100, and N145 components show a significant difference in normal, cochlear, and deaf. The early diagnosis of hearing impairment demands the patients and doctors to make a series of decisions for the betterment of the implantee in an accelerated manner through traditional database methodology implemented for making decisions, analyzing, interpreting, processing of data is so difficult in the conventional system. Medical knowledge represented for the computers to analyze the inferred data and to make the decisions. Ontology is the most potent tool to encode medical data semantically. A fuzzy ontology-based decision support system built to predict the cochlear implantation outcome. The ontology created using Protégé software tool and the decision taken using Jena and pellet reasoner. A fuzzy-based prediction model designed using a fuzzy interface system to estimate the categories of auditory performance (CAP) test reliable indicators of cochlear implantation success. [ABSTRACT FROM AUTHOR]