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

People often query various Points of Interest (POIs) to plan their city tour itineraries using location-based services that provide reviews and ratings of various attributes of a POI. Most traditional location-based recommendation systems focus on various attributes like rating, cost, and social similarities of either POIs, users, or both but do not focus on efficient retrieval of POIs. This paper proposes a spatial index structure uR-tree (utility-based R-tree) for efficiently processing a location-based query implied in a location-aware recommender system to select multiple POIs which a user can visit in a tour. The uR-tree is constructed considering a change in static attributes like availability of parking, basic amenities, etc., and dynamic attributes like ticket price, congestion, etc., of each POI with the aim to maximize a user’s experience. At first, the utility of each POI is calculated at different points of time based on its static and dynamic attributes. A uR-tree is constructed for the systematic indexing of the utility of each POI, and an algorithm is applied for the retrieval of top-k maximum utility POI in different time-intervals. Then, we propose two Utility-based trip scheduling algorithms for distance-based travel recommendations. The experiments were conducted on real-world location-based social network data sets and show that the proposed scheme has a lower response time, Disk I/Os, and a higher query success rate as compared to state-of-the-art, R-tree index structure.

(SoE) School of Engineering

Multimedia tools and Application

uR-tree: a spatial index structure for handling multiple point selection queries

Fantasy Sports are becoming more and more popular these days, hence the race to crack it is more trending than ever. In this paper, we focus on cricket (IPL) and Dream11. Using advanced statistical and graphical models, and new performance metrics for batting and bowling we aim to build a model that can predict the top performing 11 players out of the two teams. This involves predicting the player performance and selecting the best 11 while complying with league constraints. The proposed model on an average predicts 70% of the players from the Dream Team.

Research Challenges in Information Science

Team Selection Using Statistical and Graphical Approaches for Cricket Fantasy Leagues

We propose a novel video sampling scheme for human action recognition in videos, using Gaussian Weighing Function. Traditionally in deep learning-based human activity recognition approaches, either a few random frames or every $k^{th}$ frame of the video is considered for training the 3D CNN, where $k$ is a small positive integer, like $4$, $5$, or $6$. This kind of sampling reduces the volume of the input data, which speeds-up the training network and also avoids overfitting to some extent, thus enhancing the performance of the 3D CNN model. In the proposed video sampling technique, consecutive $k$ frames of a video are aggregated into a single frame by computing a Gaussian-weighted summation of the $k$ frames. The resulting frame preserves the information in a better way than the conventional approaches and experimentally shown to perform better. In this paper, a 3-Dimensional deep CNN is proposed to extract the spatio-temporal features and follows Long Short-Term Memory (LSTM) to recognize human actions. The proposed 3D CNN architecture is capable of handling the videos where the camera is placed at a distance from the performer. Experiments are performed with KTH, WEIZMANN, and CASIA-B Human Activity and Gait datasets, whereby it is shown to outperform state-of-the-art deep learning based techniques. We achieve 95.78%, 95.27%, and 95.27% over the KTH, WEIZMANN, and CASIA-B human action and gait recognition datasets, respectively. &nbsp;&nbsp;

World University root node

Multimedia Tools and Applications

An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos

Road accidents caused by several factors are responsible for the deaths and injuries of people. The majority of deaths occur within first few hours of an accident. If in case of an accident the victim becomes incapacitated, and there is no onlooker to help, then an accurate automated accident reporting system can significantly reduce the number of such deaths. This paper reports an Android smartphone-based end-to-end Internet of Things (IoT) system that can transmit accident information to emergency services and affected families once a vehicle accident is detected. Along with the detection, the classification of vehicle accidents can be very helpful in identifying suitable life-saving medical-aid and appropriate rescue operation equipment. The main objective of this research work is to develop a machine learning (ML) model that can detect as well as classify vehicle accidents accurately into eight categories. To enhance the classification efficacy of the system, a multi-sensor fusion framework has been proposed that incorporates various sensor-fusion techniques at different levels of its implementation along with several preprocessing methods such as 10-ms moving maximum, complementary filters, and a 2-sec sliding window. The framework uses Logistic Regression (LR) based stacked generalization approach to combine the decisions of three ML classifiers, which are based on Decision Tree (DT), Naïve Bayes (NB), and Random Forest (RF) methods. The LR-based stacking classifier is found to be very accurate with an F1-score of 0.95, whose performance is significantly better than the performance of all individual base-classifiers. © 2022 Elsevier B.V.

Future Generation Computer Systems

Vehicle accident sub-classification modeling using stacked generalization: A multisensor fusion approach

Poorly constructed roads, as well as roads that are damaged with cracks or potholes, are some of the leading causes of road accidents, particularly in developing nations like India. Accurate assessment of road conditions and prompt transmission of such situations may allow interested agencies to take corrective action, reducing the number of road accidents and fatalities. With the rapid advancements in sensor technologies, data aggregation, and fusion algorithms, it is now possible to identify such road conditions and report them for prompt action by the concerned authorities. Using mobile sensing, this study aims to collect and monitor the road surface conditions in the city of Dehradun, Uttarakhand, India. In order to identify different road surfaces such as smooth roads, speed hump, and rumble strips, the collected data has been analyzed using the Artificial Neural Network technique. The proposed model has an accuracy of 97%, demonstrating the algorithm's effectiveness in detecting road surface conditions. © 2022 IEEE.

2022 14th International Conference on COMmunication Systems and NETworkS, COMSNETS 2022

Modeling IoT Enabled Classification System for Road Surface Monitoring

The Billboard magazine is a world renowned music publication since 1984 and it releases a weekly ranking of the top 100 songs in various categories such as rock, pop, hip-hop, etc. Several studies have determined that it is possible to predict the approximate bucket of ranks that a song is likely to chart in using social and subjective indicators. The definitions of these indicators however can change over time, thus rendering the previous classifications erroneous. Here, we report successful results from our experiments in predicting the ranks and the number of weeks the songs are likely to stay on the charts, using objective and well-defined features, obtained from Spotify’s Web API. It extends existing research about classifying songs into rank buckets of Top-10 and Top-40 using these objective features, demonstrating that it is possible to predict exact ranks of the songs within a root-mean-squared-error of 28 ranks and the number of weeks of charting within a root-mean-squared-error of 7 weeks, demonstrating definitive trends between individual features and the ranks of the songs, demonstrating that objective metadata about the songs serve as good indicators about the trends in the Billboard charts, and can be used to predict a song’s performance on the charts within acceptable error rates. © 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)

Performance Prediction of Songs on Online Music Platforms

There have been various attempts to leverage the massive amount of data generated from social media websites. The real-time nature of social media platforms can help detect events, especially in a metropolitan city. In this paper, a system is proposed, that detects traffic-related events and road conditions in real-time from tweets by using classification algorithms and custom-trained named entity recognition model (NER) to classify and extract contextual information and visualise it on a map to get an overall picture of the traffic conditions in a city. The proposed system is versatile and can be applied to other use cases such as detecting calamities, social unrest, etc. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Real-Time Detection and Visualization of Traffic Conditions by Mining Twitter Data

Network science is a domain in which we focus on studying complex networks like social networks, chemical networks, computer networks, telecommunication networks, cognitive networks, semantic networks, and biological networks. In recent years, link prediction in complex networks has become an active research field because of its various real-world applications. In this paper, we present a novel algorithm for link prediction influenced by the concept of vertex entropy and ego networks. We used 12 real-world datasets to evaluate the performance of the novel algorithm. Results are compared with the 12 baseline algorithm based on 4 metrics AUC, Precision, Prediction-Power, and Precision@K. Experimental results show the effectiveness of the novel algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Studies in Computational Intelligence

Vertex Entropy Based Link Prediction in Unweighted and Weighted Complex Networks

Skin cancer is considered to be the most common human malignancy. Around 5 million new cases of skin cancer are recorded in the United States annually. Early identification and evaluation of skin lesions are of great clinical significance, but the disproportionate dermatologist-patient ratio poses a significant problem in most developing nations. Therefore a novel deep architecture, named as SkiNet, is proposed to provide faster screening solution and assistance to newly trained physicians in the process of clinical diagnosis of skin cancer. The main motive behind SkiNet’s design and development is to provide a white box solution, addressing a critical problem of trust and interpretability which is crucial for the wider adoption of Computer-aided diagnosis systems by medical practitioners. The proposed SkiNet is a two-stage pipeline wherein the lesion segmentation is followed by the lesion classification. Monte Carlo dropout and test time augmentation techniques have been employed in the proposed method to estimate epistemic and aleatoric uncertainty. A novel segmentation model named Bayesian MultiResUNet is used to estimate the uncertainty on the predicted segmentation map. Saliency-based methods like XRAI, Grad-CAM and Guided Backprop are explored to provide post-hoc explanations of the deep learning models. The ISIC-2018 dataset is used to perform the experimentation and ablation studies. The results establish the robustness of the proposed model on the traditional benchmarks while addressing the black-box nature of such models to alleviate the skepticism of medical practitioners by incorporating transparency and confidence to the model’s prediction. Copyright: © 2022 Singh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Fulltext

PLoS ONE

SkiNet: A deep learning framework for skin lesion diagnosis with uncertainty estimation and explainability

Understanding software size distribution is critical to software cost estimation using COCOMO model and design of reliable production function model. This paper proposes and validates a theoretical framework based on the maximization of Shannon entropy to learn component size distribution of software systems when partial information about the moments is given. Specification of appropriate moment constraints either in the form of shifted geometric mean or arithmetic mean or both geometric and arithmetic means are considered. The models are validated using 30 real datasets. The analysis reveals that software systems where component sizes depict power-law behavior are governed by shifted geometric mean whereas those systems in which component size distribution shows exponential behavior are described by arithmetic mean. Another type of software system is also considered where the component size distribution is found to depict gamma distribution. Such systems are characterized by specification of both arithmetic and geometric means. The study underlines that the use of modern object-oriented programming languages adheres to power-law distribution indicating the existence of team synergies leading to substantial containment of software costs when compared to the use of traditional procedural programming languages. © 1976-2012 IEEE.