Indian Institute of Information Technology and Management, Gwalior

About: Indian Institute of Information Technology and Management, Gwalior is a education organization based out in Gwalior, Madhya Pradesh, India. It is known for research contribution in the topics: Wireless sensor network & Density functional theory. The organization has 717 authors who have published 1259 publications receiving 10928 citations.

Inertia Weight strategies in Particle Swarm Optimization

Abstract: Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.

Defect‐Engineered Graphene for High‐Energy‐ and High‐Power‐Density Supercapacitor Devices

Abstract: Defects are often written off as performance limiters. Contrary to this notion, it is shown that controlling the defect configuration in graphene is critical to overcome a fundamental limitation posed by quantum capacitance and opens new channels for ion diffusion. Defect-engineered graphene flexible pouch capacitors with energy densities of 500% higher than the state-of-the-art supercapacitors are demonstrated.

Anonymity Preserving IoT-Based COVID-19 and Other Infectious Disease Contact Tracing Model

Abstract: Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.

Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering

Abstract: A novel hybrid dimension reduction method is proposed.It obtains a highly informed and much reduced feature subset.It improves obtained results of the underlying clustering method.It improves computational complexity of the underlying clustering method. High dimensionality of the feature space is one of the major concerns owing to computational complexity and accuracy consideration in the text clustering. Therefore, various dimension reduction methods have been introduced in the literature to select an informative subset (or sublist) of features. As each dimension reduction method uses a different strategy (aspect) to select a subset of features, it results in different feature sublists for the same dataset. Hence, a hybrid approach, which encompasses different aspects of feature relevance altogether for feature subset selection, receives considerable attention. Traditionally, union or intersection is used to merge feature sublists selected with different methods. The union approach selects all features and the intersection approach selects only common features from considered features sublists, which leads to increase the total number of features and loses some important features, respectively. Therefore, to take the advantage of one method and lessen the drawbacks of other, a novel integration approach namely modified union is proposed. This approach applies union on selected top ranked features and applies intersection on remaining features sublists. Hence, it ensures selection of top ranked as well as common features without increasing dimensions in the feature space much. In this study, feature selection methods term variance (TV) and document frequency (DF) are used for features' relevance score computation. Next, a feature extraction method principal component analysis (PCA) is applied to further reduce dimensions in the feature space without losing much information. The effectiveness of the proposed method is tested on three benchmark datasets namely Reuters-21,578, Classic4, and WebKB. The obtained results are compared with TV, DF, and variants of the proposed hybrid dimension reduction method. The experimental studies clearly demonstrate that our proposed method improves clustering accuracy compared to the competitive methods.