Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.

Shiv Nadar University

Day by day, the number of blog users and microblog users is increasing worldwide. It is easy to say that blogs have captured a significant portion of other web services. In the past few years, the number of users has exponentially increased. User count of Facebook, Twitter, and Instagram applications is not hidden from anyone. Users on such platforms share ideas, experiences, stories, opinions, and views and want to interact with people with the same set of interests. As per the user's expectation, there is a requirement of two things: content curation and recommendations. The content curation algorithm will find the people and their posts on personalized search results. In addition, the recommendation system will help to find the most appropriate match to interact with. In this paper, both approaches are combined to show the user's curated and recommended results. The article focuses on the hybrid model named S-ANFIS, and the results are compared with the well-known approaches like ANN, Deep Neural Network (DNN), and Recurrent Neural Network (RNN). © 2022 Harsh Khatter et al.

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Journal of Sensors

Adaptive and Personalized Web Blog Searching Technique Using S-ANFIS

In drug review sentimental analysis (SA), users can share their experiences after consuming the drugs, which provides an accurate decision about the safety of the drug and public health. Patient-written medical and health-care reviews are among the most valuable and informative textual content on social media, but researchers in the areas of natural language processing (NLP) and data mining have not researched them thoroughly. These reviews provide insight into patients' interactions with doctors, treatment, and satisfaction or dissatisfaction with health services. The existing approaches have some problems like exploding/vanishing gradients and do not have sequential modeling. When learning long reviews, the exploding and vanishing gradient problems occurs. This problem makes it hard to tune parameters and learn in the network. The existing methods do not have sequential modeling because they fail to extract long dependencies for long reviews in both backward and forward directions. To overcome these issues, we proposed a Modular Lexicon Generation and a Fusion of Bidirectional threshold weighted mapping CNN-RNN (MLBTWCR) for classifying drug reviews based on users opinions. The Aspect based Modular Lexicon generation using the Advanced Dragon Fly Algorithm (AMLDA) is used to generate the score values for the lexicon and labels based on aspect. The Bidirectional Dropout Long and Short-Term Memory (Bi-DLSTM) and Bidirectional Gated Recurrent Unit (Bi-GRU) used for extracting long dependencies and for performing the sequence of arbitrary length in both backward and forward directions. The experimental results are evaluated using Drugslib.com and Drugs.com datasets. Based on evaluation result, the proposed MLBTWCR gives accuracy of 93.02%, recall of 88.72%, error rate of 11.2, false positive rate (FPR) of 11.3, false negative rate (FNR) of 13.6, running time of 15 s, and convergence speed of 0.2 and F-measure of 92.64%. Hence, our method performs well for the drug reviews classification based on aspects. © 2022 John Wiley & Sons, Ltd.

Concurrency and Computation: Practice and Experience

Drug review sentimental analysis based on modular lexicon generation and a fusion of bidirectional threshold weighted mapping CNN-RNN

The crop diseases can’t detected accurately by only analysing separate disease basis. Only with the help of making comprehensive analysis framework, users can get the predictions of most expected diseases. In this research, IOT and machine learning based technique capable of processing acquisition, analysis and detection of crop health information in the same platform is introduced. The proposed system supports distinguished services by monitoring crop and also managed its data, devices and models. This system also supports data sharing and communication with the help of IOT using unmanned aerial vehicle (UAV) and maintains high communication standards even in bad communication environment. Therefore, IOT and machine learning ensures the high accuracy of disease prediction in crop. The proposed integrated system is capable of detecting health of crop through analysis of multi-spectral images captured through the IOT associated UAV. The various machine learning is also applied to test the performance of our system and compared with the existing disease detection methods.&nbsp;

Journal of Scientific and Industrial Research

Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System

Today, everything is progressing to ‘Smart' to enhance the environment via technological progress including IoT, big data, AI, ICT, and so on. But, in this whole process, the sustainability of being ‘smart' is implemented by the cloud-based technology, which also acts as an engine. Smart society is another live example of this era that makes potential use of digital technology and sensor devices to improve people's lives through the internet. It also incorporates cloud computing, which significantly benefits them by offering a sustainable environment to access the computing power at large scale, which they could not previously access due to lack of resources. This chapter provide a broad overview of smart society that covers the scope and services, technological pillars, features necessities to be titled as ‘smart', and a sustainable development. Also, it broadly covers the role of cloud computing, related technologies, and generic architecture for the sustainability of smart societies followed by applications with a case study and challenges.

Integration and Implementation of the Internet of Things Through Cloud Computing

Sustainability of Cloud Based Smart Society

In many existing solutions of image-to-image rendering problems, the only focus is to find the closest output of the Generative Adversarial Network (GAN). In this research article, authors propose a generative adversarial network, a solution to pixel-to-pixel rendering problems and reduced the loss function to the maximum under all interactions. For achieving the best result, we have considered the mean square loss function in the generator and binary cross for the discriminator. Our proposed model deals with not only images but also read sketches where the edges are not sharp too. We have used a facade dataset to test our proposed model.

IT in Industry

CONDITIONAL GANS AS A SOLUTION TO IMAGE-TO-IMAGE RENDERING PROBLEMS

Machine learning is the key solution to many AI issues, but learning models rely heavily on specific training data. While a Bayesian setup can be used to incorporate some learning patterns with previous knowledge, those patterns can not access any organized world knowledge on requirements. The primary objective is to enable human-capable machines in ordinary everyday circumstances to estimate and make presumptions. In this paper we propose to respond to such common sense issues through a textual inference system with external, organized common sense graphs for explanatory inferences. The framework is based on a schematic map as a pair of questions and answers, a linked subgraph from the semantine to the symbolic space of knowledge-based external information. It displays a schematic map with a new network graphic module for information knowledge and performance with graph representations. LSTMs and graphical networks with a hierarchical attention-based direction are the basis of our model. It is flexible and understandable from the intermediate attention scores, leading to confident results. We also achieved state-of-the-art reliability on CommonsenseQA, a broad database of common sense reasoning utilizing ConceptNet as the only external tool for BERT-based models. © 2021 Institute of Physics Publishing. All rights reserved.

IOP Conference Series: Materials Science and Engineering

Knowledge graph augmented advanced learning models for commonsense reasoning

Background: Automatic Machine Translation (AMT) Evaluation Metrics have become popular in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation engines and Machine Translation as a field itself. Translator is a very important tool to break barriers between communities especially in countries like India, where people speak 22 different languages and their many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates how well these are performing. This is where machine translation evaluation enters. Objective: This paper discusses the importance of Automatic Machine Translation Evaluation and compares various Machine Translation Evaluation metrics by performing Statistical Analysis on various metrics and human evaluations to find out which metric has the highest correlation with human scores. Methods: The correlation between the Automatic and Human Evaluation Scores and the correlation between the five Automatic evaluation scores are examined at the sentence level. Moreover, a hypothesis is set up and p-values are calculated to find out how significant these correlations are. Results: The results of the statistical analysis of the scores of various metrics and human scores are shown in the form of graphs to see the trend of the correlation between the scores of Automatic Machine Translation Evaluation metrics and human scores. Conclusion: Out of the five metrics considered for the study, METEOR shows the highest correlation with human scores as compared to the other metrics. © 2020 Bentham Science Publishers.

Recent Advances in Computer Science and Communications

Statistical analysis of machine translation evaluation systems for English-Hindi language pair

—Machine Translation (MT) is a programmed conversion in which computer software is utilized to convert manuscripts from one Natural Language (like English) to a different Language (such as Hindi). To process any such conversion, through human or through automatic means, the conversion must be established such that it reinstate the complete sense of a manuscript from its base (source) linguistic into the target language. In this paper, the study of prevailing evaluation systems along with assessing their performance is achieved through the similarity metrics. Moreover, the authors have also presented an improved technique of translation employing features of Natural Language Processing and consequently, to acquire an enhanced and more accurate assessing Machine Translation system, a corpus is selected and the outcomes are compared with the prevailing methods. Besides this, two well-known systems such as Google and Bing decoders are selected to inquire and to assess the study of metrics called similarity metrics through Assessment of Text Essential Characteristics score. This is found to provide more accuracy than prevailing methods. Furthermore, evaluations are tested under various metrics systems like Jaccard similarity metrics, cosine similarity metrics, and sine metrics to deliver enhanced accuracy than prevailing methods.&nbsp;

International Journal of Modern Education and Computer Science

Performance Enhancement of Machine Translation Evaluation Systems for English – Hindi Language Pair

The problem of language translation has prevailed in society for so long. However, up to some extent the problem is being reduced by the online available machine translation systems like Google, Bing, Babelfish, etc. But with the emergence of these Machine Translation Systems, there arises the problem of their validation. Can we trust on such translation systems blindly? Is there no scope of improvement? Are these Machine Translation systems not prone to errors? The answer to all these questions is No. So, for this purpose, we need a mechanism that can test or assess these Machine Translation systems. In this paper, we have proposed an algorithm that will evaluate such Machine Translation systems. Our algorithm is being compared with a very well-known BLEU algorithm that works very well for non-Indian languages. The accuracy of the designed algorithm is evaluated using the standard datasets like Tides and EMILLE. © BEIESP.

International Journal of Innovative Technology and Exploring Engineering

A better gauging model for the evaluation of automatic machine translation of english – hindi language

Assessment of the Translation done by machines has always been a major topic of interest for the Natural Language Researchers. Every day, a new translator is out there in the market claiming to be the best one. But to challenge such claims, one needs to have a powerful assessment tool that can judge the output of any translator on different parameters like fluency of the language, adequacy as well as the accuracy of the output generated. In this paper, we have discussed the major issues with the algorithms used for the evaluation of Machine translation. We have also presented a brief description and comparative study of different metrics used to evaluate the outcome of a machine translator over English to Hindi Language parallel sentences. © 2018 IEEE.

Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing, Data Science and Engineering, Confluence 2018

A Summary and Comparative Study of Different Metrics for Machine Translation Evaluation

The task of the Evaluation of Machine Translation (MT) is very difficult and challenging too. The difficulty comes from the fact that translation is not a science but it is more an art; most of the sentences can be translated in many acceptable forms. Consequently, there is no such fix standard against which a particular translation can be evaluated. If it has been possible to make an independent algorithm that would be able to evaluate a specific Machine Translation, then the belief is that this evaluation algorithm will be a better algorithm than the translating algorithm itself. Initially, MT evaluation used to be done by human beings which was a time-consuming task and highly subjective too. Also evaluation results may vary from one human evaluator to another for the same sentence pair. Therefore we need automatic evaluation systems, which are quick and objective. Different methods for Automatic Evaluation of Machine Translation have been projected in recent years, out of which many of them have been accepted willingly by the MT community. In the proposed work, we have checked the applicability of BLEU metric and of its modified versions for English to Hindi Machine Translation(s) particularly for Agriculture Domain. Further, we have incorporated some additional features like synonym replacement and shallow parsing modules and after that we have calculated the final score by using BLEU and M-BLEU metrics. The sentences which have been tested are taken from Agriculture Domain. The BLEU metric does not consider the synonym problem and it considers synonym as different words thereby lowering down the final calculated score while comparing the human translations with the machine translations. To overcome this drawback of BLEU, we have incorporated a synonym replacement module in our algorithm. For this, first of all the word is replaced by its synonym present in any of the reference human translations and then it is compared with the reference human translation. © 2016 IEEE.

Proceeding - IEEE International Conference on Computing, Communication and Automation, ICCCA 2016

An improvement in BLEU metric for English-Hindi machine translation evaluation

Machine Translation, sometimes referred by the acronym MT, is one of the important fields of study of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of atomic words in one natural language for words in another language. Around the world, numerous systems are available in the market for the assessment of the translation being done by the various translation systems. Even within India, a large number of such evaluation systems are available and a lot of research is still going on to develop a better evaluation system which can beat the results produced by Human Evaluators. Even the main challenge before Indian Researchers is that the evaluation systems which are giving unbeatable results for the translation of Foreign languages (such as German, French, Chinese, etc.) are not even giving considerable results for the translation of Indian Languages (Hindi, Tamil, Telugu, Punjabi, etc.). So at par these evaluation systems cannot be applied as it is to evaluate Machine Translations of Indian Languages. Indian languages require a novel approach because of the relatively unrestricted order of words within a word group. In this paper, we are presenting an algorithm (by incorporating different modules of language models like synonym replacement, root word extraction and shallow parsing) which when applied upon the translation of English to Hindi text gives better evaluation results as compared to those algorithms which do not incorporate all these modules. Moreover, our study is limited to English to Hindi language pair and the testing is being with the corpora of agriculture domain. © 2017 IEEE.