A Survey of MANET Survivability Routing Techniques

Mobile ad hoc networks have a wide range of application usage today, due to its great services, easy installation and configuration, and its other distinctive characteristics. In contrast, the attackers also have developed their own way to disrupt MANET normal operations. Many techniques, approaches and protocols have proposed to support Mobile Ad hoc Network (MANET) survivability in adversarial environment. Survivable of routing operations is the key aspects of the challenge in MANETs because most of destructive attacks classified as active attacks and all are intent to attack MANET routing operation to prevent it from providing it services in a right time. In this paper, we will discuss the most effective and practical initiatives have designed to keep MANET survive in an adversarial environment and how it supporting MANET availability.

Carving Thumbnail/s and Embedded JPEG Files Using Image Pattern Matching

Images (typically JPEG) are used as evidence against cyber perpetrators. Typically the file is carved using standard patterns. Many concentrate on carving JPEG files and overlook the important of thumbnail in assisting forensic investigation. However, a new unique pattern is used to detect thumbnail/s and embedded JPEG file. This paper is to introduce a tool call PattrecCarv to recognize thumbnail/s or embedded JPEG files using unique hex patterns (UHP). A tool called PattrecCarv is developed to automatically carve thumbnail/s and embedded JPEG files using DFRWS 2006 and DFRWS 2007 datasets. The tool successfully recovers 11.5% more thumbnails and embedded JPEG files than PredClus.

Thermal-aware relocation of servers in green data centers

Rise in inlet air temperature increases the corresponding outlet air temperature from the server. As an added effect of rise in inlet air temperature, some active servers may start exhaling intensely hot air to form a hotspot. Increase in hot air tem- perature and occasional hotspots are an added burden on the cooling mechanism and result in energy wastage in data centers. The increase in inlet air temperature may also result in failure of server hardware. Identifying and comparing the thermal sensi- tivity to inlet air temperature for various servers helps in the thermal-aware arrangement and location switching of servers to minimize the cooling energy wastage. The peak outlet temperature among the relocated servers can be lowered and even be homogenized to reduce the cooling load and chances of hotspots. Based upon mutual comparison of inlet temperature sensitivity of heterogeneous servers, this paper presents a proactive approach for thermal-aware relocation of data center servers. The experimental results show that each relocation operation has a cooling energy saving of as much as 2.1 kW·h and lowers the chances of hotspots by over 77%. Thus, the thermal-aware relocation of servers helps in the establishment of green data centers.

A review of interoperability assessment models

Interoperability is the ability of systems to provide services to and accept services from other systems,and to use the services exchanged so as to operate together in a more effective manner.The fact that interoperability can be improved means that the metrics for measuring interoperability can be defined.For the purpose of measuring the interoperability between systems,an interoperability assessment model is required.This paper deals with the existing interoperability assessment models.A comparative analysis among these models is provided to evaluate the similarities and differences in their philosophy and implementation.The analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability assessment model.

A Convolutional Deep Neural Network Approach for miRNA Clustering

The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. Clustering is a cornerstone in bioinformatics research, offering a potent computational tool for analyzing diverse types of data encountered in genomics and related fields. MiRNA clustering plays a pivotal role in deciphering the intricate regulatory roles of miRNAs in biological systems. It uncovers novel biomarkers for disease diagnosis and prognosis and advances our understanding of gene regulatory networks and pathways implicated in health and disease, as well as drug discovery. Namely, we have implemented clustering procedure to find interrelations among miRNAs within clusters, and their relations to diseases. Deep clustering (DC) algorithms signify a departure from traditional clustering methods towards more sophisticated techniques, that can uncover intricate patterns and relationships within gene expression data. Deep learning (DL) models have shown remarkable success in various domains, and their application in genomics, especially for tasks like clustering, holding immense promise. The deep convolutional clustering procedure used is different from other traditional methods, demonstrating unbiased clustering results. In the paper, we implement the procedure on a Multiple Myeloma miRNA dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues.