Next-generation cybersecurity through a blockchain-enabled federated cloud framework

Minimizing the breach detection gap (BDG) for cyber-attacks is a big concern for all organizations and governments. Cyber-attacks are discovered daily, many of which have gone undetected for days to years before the victim organizations detect and deploy the cyber defense. Cyber defense solutions are advancing to combat risks and attacks from traditional to next-generation advanced defense protection solutions. However, many individuals, organizations and businesses continue to be hit by new waves of global cyber-attacks. In this paper, we present a blockchain-enabled federated cloud computing framework that uses the Dempster–Shafer theory to reduce BDG by continuously monitoring and analyzing the network traffics against cyber-attacks. We evaluate the proposed approach using numerical results, and the proposed approach outperforms the traditional approaches.     

A systematic analysis using classification machine learning algorithms to understand why learners drop out of MOOCs

Neural Computing and Applications - The increasing popularity of massively online open courses (MOOCs) has been attracting a lot of learners. Despite the popularity, it has been observed that there...     

Soft computing approaches for image segmentation: a survey

Multimedia Tools and Applications - Image segmentation is the method of partitioning an image into a group of pixels that are homogenous in some manner. The homogeneity dependents on some...     

Biotechnological potential of microbial inulinases: Recent perspective

Among microbial enzymes, inulinases or fructo-furanosylhydrolases have received considerable attention in the past decade, and as a result, a variety of applications based on enzymatic hydrolysis of inulin have been documented. Inulinases are employed for generation of fructose and inulo-oligosaccharides (IOS) in a single-step reaction with specificity. The high fructose syrup can be biotransformed into value-added products such as ethanol, single cell protein, while IOS are indicated in nutraceutical industry as prebiotic. Myriad microorganisms produce inulinases, and a number of exo- and endo-inulinases have been characterized and expressed in heterologous hosts. Initially, predominated by Aspergilli, Penicillia, and some yeasts (Kluyveromyces spp.), the list of prominent inulinase producers has gradually expanded and now includes extremophilic prokaryotes and marine-derived microorganisms producing robust inulinases. The present paper summarizes important developments about microbial inulinases and their applications made in the last decade.     

A Cross-Layer and Optimized Privacy Method in Vehicular Ad-Hoc Networks

Vehicular networks has been recently proposed to connect vehicles and form ad-hoc networks. Many safety and information-entertainment related applications have thus been proposed. These applications, however, potentially have unique privacy challenges. For example, the vehicle’s network identity is strictly linked to the owner’s identity due to the insurance liability. In this paper, we propose cross-layer privacy protection protocols including initialization, joining and exiting protocols. In addition, we also analytically discuss the optimization of the quantity of pseudonyms to save costs. The analytical and numerical results showed the effectiveness of the proposed methods.     

Second Order Training of a Smoothed Piecewise Linear Network

In this paper, we introduce a smoothed piecewise linear network (SPLN) and develop second order training algorithms for it. An embedded feature selection algorithm is developed which minimizes training error with respect to distance measure weights. Then a method is presented which adjusts center vector locations in the SPLN. We also present a gradient method for optimizing the SPLN output weights. Results with several data sets show that the distance measure optimization, center vector optimization, and output weight optimization, individually and together, reduce testing errors in the final network.     

Performance analysis of pseudo 4-phase dual-rail asynchronous protocol

This article presents the performance analysis of novel pseudo 4-phase dual-rail protocol with self-reset and multiple-reset logic for high speed asynchronous applications. The self-reset logic eliminates the need for separate empty phase and hence reduces the number of transitions at the input and the output. The multiple-reset logic reduces the reset phase and hence increases the throughput. The performance of the pseudo 4-phase dual-rail protocol is compared with the existing 4-phase dual-rail protocol by implementing 8-bit and 16-bit asynchronously pipelined carry look ahead adders (CLA) in 0.35?µm technology. The time period decreases up to 32.58% and 35.93% respectively, the reset phase reduces up to 66.39% and 76.18%, respectively, and the operating frequency increases up to 48.4% and 56.2%, respectively, for 8- and 16-bit CLA adders with the pseudo 4-phase dual-rail protocol compared to the conventional 4-phase dual-rail protocol at the cost of increase in area of 8.8% and 3.3%, respectively. The delay-power product in ns-mW of the 8- and 16-bit CLAs is reduced at the maximum of 11.29% and 23.74%, respectively, with the pseudo 4-phase dual-rail protocol compared to the conventional 4-phase dual-rail protocol. The pseudo 4-phase dual-rail protocol is suitable for interfacing with synchronous environments.