Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.     

Cumulative voting consensus method for partitions with variable number of clusters

Over the past few years, there has been a renewed interest in the consensus clustering problem. Several new methods have been proposed for finding a consensus partition for a set of n data objects that optimally summarizes an ensemble. In this paper, we propose new consensus clustering algorithms with linear computational complexity in n. We consider clusterings generated with random number of clusters, which we describe by categorical random variables. We introduce the idea of cumulative voting as a solution for the problem of cluster label alignment, where, unlike the common one-to-one voting scheme, a probabilistic mapping is computed. We seek a first summary of the ensemble that minimizes the average squared distance between the mapped partitions and the optimal representation of the ensemble, where the selection criterion of the reference clustering is defined based on maximizing the information content as measured by the entropy. We describe cumulative vote weighting schemes and corresponding algorithms to compute an empirical probability distribution summarizing the ensemble. Given the arbitrary number of clusters of the input partitions, we formulate the problem of extracting the optimal consensus as that of finding a compressed summary of the estimated distribution that preserves maximum relevant information. An efficient solution is obtained using an agglomerative algorithm that minimizes the average generalized Jensen-Shannon divergence within the cluster. The empirical study demonstrates significant gains in accuracy and superior performance compared to several recent consensus clustering algorithms.     

A Report on CNSM 2010

The 6th International Conference on Network and Service Management (CNSM 2010) was held on October 25–29, 2010 in Niagara Falls, Canada. CNSM is a premier annual conference, sponsored by IEEE Communications Society and IFIP Working Group on Network and Distributed Systems Management, in the general area of network, systems, and service management. Built upon the success starting in 2005 to collocate six management workshops within the same week (MANWEEK), this year we debuted the single track conference CNSM ().     

Gesture behavior in unmedicated schizotypal adolescents.

Schizotypal personality disorder is characterized by interpersonal and verbal communication deficits. Despite the important role of gesture in social communication, no published reports examine the use of gesture by individuals with SPD. In this study, raters code gesture from videotaped interviews of unmedicated adolescents with SPD, other personality disorders, or no Axis II disorder. Results indicate that SPD adolescents show significantly fewer gestures but do not differ from the other groups in overall rate of movement. The findings are discussed in light of brain regions involved in dysfunction, parallels to schizophrenia, and treatment implications. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Designing tributary networks with multiple ring families

Advances in telecommunication technology result in improved service, but can also lead to difficult and challenging network design problems. For example, networks in which nodes are connected by rings of optical fiber can now be used to provide rapid service restoration in the event of a failure. However, as a result, network designers are faced with the new problem of designing networks based on topological ring structures. In this paper, we consider the particular case of tributary network design. In a tributary network, a group of nodes are connected to a hub node, which is used as a point of interconnection with other parts of the network. For a particular network architecture, we describe an algorithm to determine how many topological ring structures are required, and which nodes should be included on each. We highlight connections between this problem and problems in vehicle routing.A common architecture for a telecommunications network consists of several tributary (often called access) networks, which connect locations to hubs, and a backbone network, which interconnects the hubs. This paper describes a heuristic approach for designing tributary networks based on self-healing rings (SHRs). The tributary network consists of multiple ring families, and each of those is comprised of one or more SHRs, called “stacked” rings. The SHRs in a given ring family are routed over the same cycle of optical fiber cables, but each SHR serves only a subset of the locations along the cycle. Each demand location is assigned to a single SHR on one of the ring families, whereas the hub is assigned to all SHRs on all ring families. A link that is used by some ring family incurs a fixed cost plus a variable cost per SHR associated with that family. Each SHR is constrained by the demand volume it can handle and by the number of locations it can serve. This tributary ring network design problem can be viewed as a complex version of a vehicle routing problem with a single-depot andmultiple vehicles. Our algorithm is initiated with numerous ring families. It then attempts to merge these families, while ensuring that savings are realized in terms of the sum of fixed and variable costs.     

Computerized Detection of Limbal Stem Cell Deficiency from Digital Cornea Images

Limbal Stem Cell Deficiency (LSCD) is an eye disease that can cause corneal opacity and vascularization. In its advanced stage it can lead to a degree of visual impairment. It involves the changing in the semispherical shape of the cornea to a drooping shape to downwards direction. LSCD is hard to be diagnosed at early stages. The color and texture of the cornea surface can provide significant information about the cornea affected by LSCD. Parameters such as shape and texture are very crucial to differentiate normal from LSCD cornea. Although several medical approaches exist, most of them requires complicated procedure and medical devices. Therefore, in this paper, we pursued the development of a LSCD detection technique (LDT) utilizing image processing methods. Early diagnosis of LSCD is very crucial for physicians to arrange for effective treatment. In the proposed technique, we developed a method for LSCD detection utilizing frontal eye images. A dataset of 280 eye images of frontal and lateral LSCD and normal patients were used in this research. First, the cornea region of both frontal and lateral images is segmented, and the geometric features are extracted through the automated active contour model and the spline curve. While the texture features are extracted using the feature selection algorithm. The experimental results exhibited that the combined features of the geometric and texture will exhibit accuracy of 95.95%, sensitivity of 97.91% and specificity of 94.05% with the random forest classifier of n = 40. As a result, this research developed a Limbal stem cell deficiency detection system utilizing features’ fusion using image processing techniques for frontal and lateral digital images of the eyes.     

Medical image watermarking for ownership & tamper detection

Multimedia Tools and Applications - Image watermarking can provide ownership identification as well as tamper protection. Transform domain based image watermarking has been proven to be more robust...     

Optimization of user behavior based handover using fuzzy Q-learning for LTE networks

In LTE networks, handover optimization is necessary to enhance the users’ satisfaction. Specifically, users using real time traffic need to experience continuous connectivity. Hence, radio link failures (RLFs) severely affect their quality of experience. Decreasing the RLFs for non-real time users is not as urgent as the case of real time users. On the other hand, a total network collapse can happen in case of too much unnecessary handovers (ping-pongs). In this work, fuzzy Q-learning is used to optimize the two contradictory handover problems, which are RLFs and ping-pongs. The former needs to decrease Handover Margin (HOM) to reduce the too late handover, and the latter needs to increase the HOM to reduce the unnecessary signaling. In the developed algorithm, the users in the network are divided into four categories, according to their speed and the data traffic used. This increases the satisfaction of some users, while keeping the overall handover problems within acceptable limits. For each category of users, fuzzy Q-learning is applied with a different initial candidate fuzzy actions. The proposed technique shows the best performance for each category of users in terms of the most preferred metric, either decreasing RLF or decreasing ping-pongs, for this category of users in comparison with two other literature techniques, or without using any optimization technique. Moreover, the algorithm is robust against changes in the number of users in the system, as it maintains the best solution when the number of users is halved or even doubled.     

Magnetic field effects as a result of the radical pair mechanism are unlikely in redox enzymes

Environmental exposure to electromagnetic fields is potentially carcinogenic. The radical pair mechanism is considered the most feasible mechanism of interaction between weak magnetic fields encountered in our environment and biochemical systems. Radicals are abundant in biology, both as free radicals and reaction intermediates in enzyme mechanisms. The catalytic cycles of some flavin-dependent enzymes are either known or potentially involve radical pairs. Here, we have investigated the magnetic field sensitivity of a number of flavoenzymes with important cellular roles. We also investigated the magnetic field sensitivity of a model system involving stepwise reduction of a flavin analogue by a nicotinamide analogue—a reaction known to proceed via a radical pair. Under the experimental conditions used, magnetic field sensitivity was not observed in the reaction kinetics from stopped-flow measurements in any of the systems studied. Although widely implicated in radical pair chemistry, we conclude that thermally driven, flavoenzyme-catalysed reactions are unlikely to be influenced by exposure to external magnetic fields.