No abstract

Specialized data preparation techniques, ranging from data cleaning, outlier detection, missing value imputation, feature selection (FS), amongst others, are procedures required to get the most out of data and, consequently, get the optimal performance of predictive models for classification tasks. FS is a vital and indispensable technique that enables the model to perform faster, eliminate noisy data, remove redundancy, reduce overfitting, improve precision and increase generalization on testing data. While conventional FS techniques have been leveraged for classification tasks in the past few decades, they fail to optimally reduce the high dimensionality of the feature space of texts, thus breeding inefficient predictive models. Emerging technologies such as the metaheuristics and hyper-heuristics optimization methods provide a new paradigm for FS due to their efficiency in improving the accuracy of classification, computational demands, storage, as well as functioning seamlessly in solving complex optimization problems with less time. However, little details are known on best practices for case-to-case usage of emerging FS methods. The literature continues to be engulfed with clear and unclear findings in leveraging effective methods, which, if not performed accurately, alters precision, real-world-use feasibility, and the predictive model's overall performance. This paper reviews the present state of FS with respect to metaheuristics and hyper-heuristic methods. Through a systematic literature review of over 200 articles, we set out the most recent findings and trends to enlighten analysts, practitioners and researchers in the field of data analytics seeking clarity in understanding and implementing effective FS optimization methods for improved text classification tasks.

Throughout the course of human history, owing to innovations that shape the future of mankind, many technologies have been innovated and used towards making people’s lives easier. Such technologies have made us who we are today and are involved with every domain that is vital for human survival such as agriculture, healthcare, and transportation. The Internet of Things (IoT) is one such technology that revolutionizes almost every aspect of our lives, found early in the 21st century with the advancement of Internet and Information Communication (ICT) Technologies. As of now, the IoT is served in almost every domain, as we mentioned above, allowing the connectivity of digital objects around us to the Internet, thus allowing the remote monitoring, control, and execution of actions based on underlying conditions, making such objects smarter. Over time, the IoT has progressively evolved and paved the way towards the Internet of Nano-Things (IoNT) which is the use of nano-size miniature IoT devices. The IoNT is a relatively new technology that has lately begun to establish a name for itself, and many are not aware of it, even in academia or research. The use of the IoT always comes at a cost, owing to the connectivity to the Internet and the inherently vulnerable nature of IoT, wherein it paves the way for hackers to compromise security and privacy. This is also applicable to the IoNT, which is the advanced and miniature version of IoT, and brings disastrous consequences if such security and privacy violations were to occur as no one can notice such issues pertaining to the IoNT, due to their miniaturized nature and novelty in the field. The lack of research in the IoNT domain has motivated us to synthesize this research, highlighting architectural elements in the IoNT ecosystem and security and privacy challenges pertaining to the IoNT. In this regard, in the study, we provide a comprehensive overview of the IoNT ecosystem and security and privacy pertaining to the IoNT as a reference to future research.

In this paper, a framework for cryptographic protocol analysis using linear temporal logic is proposed.

Blockchain decentralization not only ensures transparency of transactions to eliminate need of trusting third party, but also makes the transactions of the network to be publicly accessible to all the participating peers in the network. As a result, data anonymity and confidentiality are compromised making several business enterprises and industrialists hesitant to adopt the technology. Although research community has proposed various privacy-preserving solutions for blockchain, however, they still lack in efficiency resulting in distrust of industries in opting for the technology. This study is conducted for contributing to the existing body of knowledge corresponding to privacy in blockchains. The fundamental goal of this study is to delve into privacy vulnerabilities of the blockchain network in a permissionless setting by identifying non-trivial roots of factors causing privacy breach in blockchain and presenting limitation of existing privacy preserving mechanisms. Studies with superficial comparison of privacy preserving techniques are available in literature but a detailed and in-depth analysis of their limitations and causes of privacy breach in blockchain is yet not done. Therefore, in this paper we first present comprehensive analysis of various privacy breaching factors of the blockchain networks. Next, we discuss existing cryptographic and noncryptographic solutions in literature. We found out that these existing privacy preserving mechanisms have their own set of limitations and hence are inefficient at current point of time. The existing privacy preserving mechanisms need further consideration of the research community before they're widely adopted and benchmarked. Therefore, in the end, we identified some future directions that need to be addressed to model an efficient privacy preserving mechanism for wider adoption of the blockchain technology.