Computer Science and Engineering

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

For a productive and healthy life, air quality plays an important role. This paper addresses the requirements to develop a system capable of providing real-time information, predictions, and alerts about the indoor environment using context-awareness. The proposed IoT system serves for data collection, pre-processing, defining rules, and forecasting the predicting states of the indoor environment by giving information to the end-user about the alerts and recommendations. A novel approach based on the indoor pollutants T, RH, CO2, PM 2.5, PM 10, and CO for the determination of the status of the environment is proposed. The pre-processing is used for filtering data using and extended Kalman filter. Further, the system uses an adaptive neuro-fuzzy inference system (ANFIS) and discrete-time Markov chains (DTMC) to predict the state of the indoor environment with the help of daily air pollution concentrations and environmental parameters. The ANFIS model predictor considers the value of indoor pollutants to form a new index: State of indoor air (SIA). For analysis and forecasting of the new index SIA, the DTMC model is used. The collected and measured data is stored in the IoT cloud using the sensing setup, and sensed information is used to develop the SIA transfer matrix, generating return durations corresponding to each SIA and providing alerts based on the data to the end-user. The models are assessed using the expected and actual return durations. The most frequent interior ventilation states, according to the predictions, are poor and moderate. Only 0.08 percent of the time does the IAQ remain in a good state. Two-thirds of the time (66%), the indoor ventilation is severe (poor, very poor, or hazardous); 19% of the time it is very bad, and 15% of the time it is hazardous, suggesting and warning that there is a very high probability of unhealthy AQI in educational institutions in the Delhi-NCR region. Performance is measured by the comparison between actual and forecasted return periods, and the forecast error for our system is low, with an absolute forecast error of 3.47% on an average. © 2022 The Author(s)

Intelligent Systems with Applications

Context-aware IoT-enabled framework to analyse and predict indoor air quality

Wireless channel characteristics are random in nature. Consideration of shadowing and fading effect in the channel is necessary so as to capture the behavior of signal variation at the receiving end. The composite effect of the phenomenon is prominent in applications where receiving nodes are deployed in deep faded region with shadowed signal propagation. This becomes more important as the average power of the signal becomes random due to fading and shadowing effects. This effect is more prominent to capture the random behavior of the channel one has to adopt the probabilistic framework by using probability distributions. The Rayleigh–lognormal distribution (RLD), useful in characterizing fading and shadowing aspects of wireless communication channel, has a complex integral having no closed form. Noting that inverse Gaussian distribution closely approximates lognormal distribution, a better approximation is proposed by generalized inverse Gaussian distribution (GIGD). While different combination of compound probability distribution is capable of capturing the behavior of the channel, no such compound distributions work for the complete range of channel variations. Thus, the probability density function of the resulting Rayleigh–generalized inverse Gaussian distribution (RGIGD), besides having a closed form and being analytically tractable, is a more appropriate substitute for RLD. Based on Kullback–Leibler (K–L) measure to assess closeness of two probability distributions, it is found that the proposed RGIGD distribution is closer to the original RLD than other distributions, viz. RGD, RIGD. Further, the proposed distribution can be considered to provide a solution for a large range variation in the sense that RGD and RIGD become its special cases and yields more realistic values of quality of service parameters along with flexibility of covering a wide range of the signal variations through its special cases. © 2021, Shiraz University.

Iranian Journal of Science and Technology - Transactions of Electrical Engineering

Composite Channel Model for Wireless Propagation with Wide-Range Signal Variation Using Rayleigh–Generalized Inverse Gaussian Distribution

Biometric security is a prominent research area with growing privacy and security concerns related to biometric data, generally known as biometric templates. Among the recently proposed biometric template protection schemes, fuzzy commitment is the most popular and reliable. It uses error correcting codes to deal with the significant number of bit errors present in the biometric templates. The high error correcting capability of the underlying error correcting codes is crucial to achieving the desired recognition performance in the biometric system. In general, it is satisfied by padding the input biometric template with some additional bits. The fixed padding approaches proposed in the literature have security vulnerabilities that could disclose the user’s biometric data to the attacker, leading to an impersonation attack. We propose a user-specific, random padding scheme that preserves the recognition performance of the system while it prevents the impersonation attack. The empirical results show that the proposed scheme provides 3 times better recognition performance on the IIT Delhi iris database than the baseline, unprotected systems. Through security analysis, we show that the attack complexity of our proposed work is 2 k, where k is the length of the secret message used to generate codeword, with k≥ 128 bits. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

On Security of Fuzzy Commitment Scheme for Biometric Authentication

Boissonnat and Pritam introduced an algorithm to reduce a filtration of flag (or clique) complexes, which can in particular speed up the computation of its persistent homology. They used so-called edge collapse to reduce the input flag filtration and their reduction method required only the 1-skeleton of the filtration. In this paper we revisit the use of edge collapse for efficient computation of persistent homology. We first give a simple and intuitive explanation of the principles underlying that algorithm. This in turn allows us to propose various extensions including a zigzag filtration simplification algorithm. We finally show some experiments to better understand how it behaves. © Marc Glisse and Siddharth Pritam; licensed under Creative Commons License CC-BY 4.0

Leibniz International Proceedings in Informatics, LIPIcs

Swap, Shift and Trim to Edge Collapse a Filtration

Alongside revenue maximization, retailers seek to offer a diverse range of items to facilitate sustainable revenue generation in the long run. Moreover, customers typically buy sets of items, i.e., itemsets, as opposed to individual items. Therefore, strategic placement of diversified and high-revenue itemsets is a priority for the retailer. Research efforts made towards the extraction and placement of high-revenue itemsets in retail stores do not consider the notion of diversification. Further, candidate itemsets generated using existing utility mining schemes usually explode; which can cause memory and retrieval-time issues. This work makes three key contributions. First, we propose an efficient framework for retrieval of high-revenue itemsets with a varying size and a varying degree of diversification. A higher degree of diversification is indicative of fewer repetitive items in the top-revenue itemsets. Second, we propose the kUI (kU tility I temset) index for quick and efficient retrieval of diverse top-λ high-revenue itemsets. We also propose the HUDIP (H igh-U tility and D iversified I temset P lacement) scheme, which exploits our proposed kUI index for placement of high-revenue and diversified itemsets. Third, our extensive performance study with both real and synthetic datasets demonstrates the effectiveness of our proposed HUDIP scheme in efficiently determining high-revenue and diversified itemsets. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Applied Intelligence

A framework for itemset placement with diversification for retail businesses

Dynamic networks continuously change their structure and properties, and anomaly detection methods must identify these structural changes at both local and global levels. Network embedding is considered a powerful tool for low-dimension structural representation of network objects. However, most embedding techniques developed for dynamic networks are incompetent in capturing the changes in global structures. Besides, with incoming edge streams, these techniques change the embeddings of all network objects, including those vertices and edges that do not experience any change in subsequent timestamps. In this paper, we propose an embedding algorithm DSEDN to identify anomalous vertices and edges in dynamic networks utilizing structural changes in networks. The algorithm uses sparse autoencoder to generate network embeddings minimizing the pair-wise and neighborhood distance between vertex representations of every subgraph derived from random walks. The subgraphs have been weighted based on their respective clustering coefficient, and the clustering algorithm is employed to identify network anomalies. The advantages of proposed method include: (i) better accuracy in detecting network anomalies; (ii) structure-preserving embeddings, such that it maintains the local and global structure of every snapshot of growing graphs; (iii) density-based embeddings; (iv) stable embeddings over time such that embeddings of consecutive timestamps do not change much for those network objects that do not experience any change; (v) scalability. We evaluate the proposed algorithm on anomaly detection and graph visualization on five real-world datasets. Our algorithm achieves improvement in AUC, scalability, and stability across all baselines. © 2022 Elsevier B.V.

Neurocomputing

Density-based structural embedding for anomaly detection in dynamic networks

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

Micro-expression has emerged as a promising modality in affective computing due to its high objectivity in emotion detection. Despite the higher recognition accuracy provided by the deep learning models, there are still significant scope for improvements in micro-expression recognition techniques. The presence of micro-expressions in small-local regions of the face, as well as the limited size of available databases, continue to limit the accuracy in recognizing micro-expressions. In this work, we propose a facial micro-expression recognition model using 3D residual attention network named MERANet to tackle such challenges. The proposed model takes advantage of spatial-temporal attention and channel attention together, to learn deeper fine-grained subtle features for classification of emotions. Further, the proposed model encompasses both spatial and temporal information simultaneously using the 3D kernels and residual connections. Moreover, the channel features and spatio-temporal features are re-calibrated using the channel and spatio-temporal attentions, respectively in each residual module. Our attention mechanism enables the model to learn to focus on different facial areas of interest. The experiments are conducted on benchmark facial micro-expression datasets. A superior performance is observed as compared to the state-of-the-art for facial micro-expression recognition on benchmark data.

ICVGIP

MERANet: Facial Micro-Expression Recognition using 3D Residual Attention Network

Mining location based social networks to understand the citizen’s check-in patterns JO &nbsp;- Computing Analyzing and understanding the movement patterns of the citizen’s with in a city, plays an important role in urban and transportation planning. Though many recent research papers focused on mining LBSN services data and performed in-depth analysis of users’ mobility patterns and their impact on their social inter-connections and friends. This paper focuses on understanding the Citizen’s movement patterns of socially interconnected users in friendship networks, by analyzing their spatial-temporal footprints/check-ins. The aim of this paper is to find the impact of structural patterns hidden in the nodes of a friendship network and external environment changes on the check-in patterns of the users. First, we classify each spatial check-in event based on its cause into either self reinforcing behavior or social influence or external stimulus. Then we mine the collective behavior of the all the users during some special events.