A classification-based privacy-preserving decision-making for secure data sharing in Internet of Things assisted applications

The introduction of the Internet of Things (IoT) paradigm serves as pervasive resource access and sharing platform for different real-time applications. Decentralized resource availability, access, and allocation provide a better quality of user experience regardless of the application type and scenario. However, privacy remains an open issue in this ubiquitous sharing platform due to massive and replicated data availability. In this paper, privacy-preserving decision-making for the data-sharing scheme is introduced. This scheme is responsible for improving the security in data sharing without the impact of replicated resources on communicating users. In this scheme, classification learning is used for identifying replicas and accessing granted resources independently. Based on the trust score of the available resources, this classification is recurrently performed to improve the reliability of information sharing. The user-level decisions for information sharing and access are made using the classification of the resources at the time of availability. This proposed scheme is verified using the metrics access delay, success ratio, computation complexity, and sharing loss.     

Anti-periodic fractional boundary value problems

We study a class of anti-periodic boundary value problems of fractional differential equations. Some existence and uniqueness results are obtained by applying some standard fixed point principles. Several examples are given to illustrate the results.     

Senti‐CS: Building a lexical resource for sentiment analysis using subjective feature selection and normalized Chi‐Square‐based feature weight generation

Sentiment analysis involves the detection of sentiment content of text using natural language processing. Natural language processing is a very challenging task due to syntactic ambiguities, named entity recognition, use of slangs, jargons, sarcasm, abbreviations and contextual sensitivity. Sentiment analysis can be performed using supervised as well as unsupervised approaches. As the amount of data grows, unsupervised approaches become vital as they cut down on the learning time and the requirements for availability of a labelled dataset. Sentiment lexicons provide an easy application of unsupervised algorithms for text classification. SentiWordNet is a lexical resource widely employed by many researchers for sentiment analysis and polarity classification. However, the reported performance levels need improvement. The proposed research is focused on raising the performance of SentiWordNet3.0 by using it as a labelled corpus to build another sentiment lexicon, named Senti‐CS. The part of speech information, usage based ranks and sentiment scores are used to calculate Chi‐Square‐based feature weight for each unique subjective term/part‐of‐speech pair extracted from SentiWordNet3.0. This weight is then normalized in a range of ?1 to +1 using min–max normalization. Senti‐CS based sentiment analysis framework is presented and applied on a large dataset of 50000 movie reviews. These results are then compared with baseline SentiWordNet, Mutual Information and Information Gain techniques. State of the art comparison is performed for the Cornell movie review dataset. The analyses of results indicate that the proposed approach outperforms state‐of‐the‐art classifiers.     

Ranking entities on the basis of users’ opinions

Online opinions are one of the most important sources of information on which users base their purchasing decisions. Unfortunately, the large quantity of opinions makes it difficult for an individual to consume in a reasonable amount of time. Unlike standard information retrieval problems, the task here is to retrieve entities whose relevance is dependent upon other people’s opinions regarding the entities and how well those sentiments match the user’s own preferences. We propose novel techniques that incorporate aspect subjectivity measures into weighting the relevance of opinions of entities based on a user’s query keywords. We calculate these weights using sentiment polarity of terms found proximity close to keywords in opinion text. We have implemented our techniques, and we show that these improve the overall effectiveness of the baseline retrieval task. Our results indicate that on entities with long opinions our techniques can perform as good as state-of-the-art query expansion approaches.     

Copper substituted nickel ferrite nanoparticles anchored onto the graphene sheets as electrode materials for supercapacitors fabrication

Nickel ferrites with high theoretical capacitance value as compared to the other metal oxides have been applied as electrode material for energy storage devices i.e. batteries and supercapacitors. High tendency towards aggregation and less specific surface area make the metal oxides poor candidate for electrochemical applications. Therefore, the improvements in the electrochemical properties of nickel ferrites (NiFe₂O₄) are required. Here, we report the synthesis of graphene nano-sheets decorated with spherical copper substituted nickel ferrite nanoparticles for supercapacitors electrode fabrication. The copper substituted and unsubstituted NiFe₂O₄ nanoparticles were prepared via wet chemical co-precipitation route. Reduced graphene oxide (rGO) was prepared via well-known Hummer's method. After structural characterization of both ferrite (Ni_1-xCu_xFe₂O₄) nanoparticles and rGO, the ferrite particles were decorated onto the graphene sheets to obtain Ni_1-xCu_xFe₂O₄@rGO nanocomposites. The confirmation of preparation of these nanocomposites was confirmed by scanning electron microscopy (SEM). The electrochemical measurements of nanoparticles and their nanocomposites (Ni_0.9Cu_0.1Fe₂O₄@rGO) confirmed that the nanocomposites due to highly conductive nature and relatively high surface area showed better capacitive behavior as compared to bare nanoparticles. This enhanced electrochemical energy storage properties of nanocomposites were attributed to the graphene and also supported by electrical (I-V) measurements. The cyclic stability experiments results showed ~65% capacitance retention after 1000 cycles. However this retention was enhanced from 65% to 75% for the copper substituted nanoparticles (Ni_0.9Cu_0.1Fe₂O₄) and 65–85% for graphene based composites. All this data suggest that these nanoparticles and their composites can be utilized for supercapacitors electrodes fabrication.     

Exploring network-level performances of wireless nanonetworks utilizing gains of different types of nano-antennas with different materials

Wireless nanonetworks are not a simple extension of traditional communication networks at the nano-scale. Owing to being a completely new communication paradigm, existing research in this field is still at an embryonic stage. Furthermore, most of the existing studies focus on performance enhancement of nanonetworks via designing new channel models and routing protocols. However, the impacts of different types of nano-antennas on the network-level performances of the wireless nanonetworks remain still unexplored in the literature. Therefore, in this paper, we explore the impacts of different well-known types of antennas such as patch, dipole, and loop nano-antennas on the network-level performances of wireless nanonetworks. We also investigate the performances of nanonetworks for different types of traditional materials (e.g., copper) and for nanomaterials (e.g., carbon nanotubes and graphene). We perform rigorous simulation using our customized ns-2 simulation to evaluate the network-level performances of nanonetworks exploiting different types of nano-antennas using different materials. Our evaluation reveals a number of novel findings pertinent to finding an efficient nano-antenna from its several alternatives for enhancing network-level performances of nanonetworks. Our evaluation demonstrates that a dipole nano-antenna using copper material exhibits around 51% better throughput and about 33% better end-to-end delay compared to other alternatives for large-size nanonetworks. Furthermore, our results are expected to exhibit high impacts on the future design of wireless nanonetworks through facilitating the process of finding the suitable type of nano-antenna and suitable material for the nano-antennas.

     

Effects of IR Laser Shots on the Surface Hardness and Electrical Resistivity of High-Purity Iron

Annealed specimens of 99.99% pure iron were irradiated with 500, 750, 1000, and 1250 Nd:YAG laser shots. The laser fluence and laser intensity at the laser irradiation spot on the target surface were 4.4 × 10³ J/cm² and 4.8 × 10¹¹ W/cm², respectively. Vickers hardness of irradiated specimens was measured at various points separated by 0.5 mm in four different mutually perpendicular directions around the laser irradiation spot. The surface hardness profile for each irradiated specimen shows an increasing trend in surface hardness till a distance of 3.5 mm from the reference point. The average surface hardness (ASH) is found to increase up to 21% and electrical resistivity increases up to 50% as the number of laser shots is increased to 1250. A linear relationship between electrical resistivity and ASH is observed. Moreover, the ASH follows the well-known Hall-Petch relation, indicating that the crystallite boundaries impede the motion of dislocations to a greater extent as the crystallite size gets smaller.