Sign language is the fundamental communication language of deaf people. Efforts to develop sign language generation systems can make the life of these people smooth and effortless. Despite the importance of sign language generation systems, there is a paucity of a systematic literature review. This is the foremost recognizable scholastic literature review of sign language generation systems. It presents a scholastic database of the literature between 1998 and 2020 and suggests classification criteria to systematize research studies. Four hundred fourteen research studies were recognized and reviewed for their direct pertinence to sign language generation systems. One hundred sixty-two research studies were subsequently chosen, examined, and classified. Each of the 162 chosen research papers was categorized based on 30 sign languages and was further comparatively analyzed based on seven comparison parameters (input form, translation technologies, application domain, use of parsers/grammars, manual/non-manual features, accuracy, and output form). It is evident from our research findings that the majority of research on sign language generation was carried out using data-driven approaches in the absence of proper grammar rules and generated only manual signs. This research study may provide researchers a roadmap toward future research directions and facilitate the compilation of information in the field of sign language generation.

Transliterating the text of a language to a foreign script is called forward transliteration and transliterating the text back to the original script is called backward transliteration. In this work, we perform both forward as well as backward transliteration on Punjabi. We transliterate Punjabi person names from Gurmukhi script to English Roman script and from English Roman script back to Gurmukhi script using n-gram language model. We used more than one million parallel entities of person names in Gurmukhi and Roman script as the training corpus. We generated English to Punjabi and Punjabi to English n-grams databases from the corpus. To get better results, we tried to create as long n-grams as possible ranging from bi-gram to 30-gram. Our n-grams database contains more than 10 million n-grams, with each n-gram having multiple mappings of the other script. The most challenging part is to find the mapping for the given n-gram from the parallel name entity while creating n-grams databases. As per the orthography rules, the same combination of letters may have different pronunciation, depending upon its location in the word. Therefore, we categorized n-grams into starting, middle, and ending n-grams and used them accordingly in the transliteration process. The transliteration process works like the merge sort. We start searching the longest possible n-gram in the database and split the string recursively until the match is found. The transliterated strings are merged back to form the final output. In English to Punjabi transliteration, we achieved 96% accuracy using gold standard and 99.14% accuracy using minimum edit distance. In Punjabi to English transliteration, the result showed 96.85% and 99.35% accuracy for the gold standard and minimum edit distance, respectively.

Current research in Internet of Things (IoT) is focused on the security enhancements to every communicated message in the network. Keeping this thought in mind, researcher in this work emphasizes on a security oriented cryptographic solution. Commonly used security cryptographic solutions are heavy in nature considering their key size, operations, and mechanism they follow to secure a message. This work first determines the benefit of applying lightweight security cryptographic solutions in IoT. The existing lightweight counterparts are still vulnerable to attacks and also consume calculative more power. Therefore, this research work proposes a new hybrid lightweight logical security framework for offering security in IoT (LLSFIoT). The operations, key size, and mechanism used in the proposed framework make its lightweight. The proposed framework is divided into three phases: registration, authentication, and light data security (LDS). LDS offers security by using unique keys at each round bearing small size. Key generation mechanism used is comparatively fast making the compromise of keys as a difficult task. These steps followed in the proposed algorithm design make it lightweight and a better solution for IoT‐based networks as compared to the existing solutions that are relatively heavy weight in nature.

Abstract Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.