Gautam Srivastava
Biographic Data
| ID | 3924887 |
|---|---|
| NAME | Gautam Srivastava |
| GIVEN NAMES | Gautam |
| FAMILY NAME | Srivastava |
| SIGNATURE | SRIVASTAVA G |
| AFFILIATIONS | China Medical University |
| ORCID | 0000-0001-9851-4103 |
| VERIFIED | Yes |
| TOTAL WORKS | 30 |
| TOTAL CITATIONS | 3 |
| WORKS AS AUTHOR | 30 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Generative AI, ESG Sensemaking, and Environmental Performance
Despite growing enthusiasm for generative artificial intelligence (GenAI) in sustainability management, it remains unclear how such technologies translate vast ESG information into meaningful environmental outcomes. This study addresses this gap by investigating how ESG sensemaking capability mediates the relationship between GenAI integration and environmental performance, analyzing how sustainability information overload moderates the relations…
Governing the Circular Shift
This study investigates how open innovation is organized and coordinated when multiple actors work together in platform‐based circular economy (CE) ecosystems. This study also examines how these ecosystem interactions and partnership dynamics shape firms' ability to generate successful circular innovations and what conditions make these effects stronger. A two‐phase mixed‐method design is used. The first phase involves semistructured interviews w…
HealthEngine
The development of more accurate and explainable health predictions is paramount in view of the increasing prevalence rates of chronic diseases and mental illnesses. Existing methods often under-utilize the rich, heterogeneous data streams coming from wearable devices, environmental sensors, and behavioral data, and hence fall short in making predictions that are both accurate and actionable. Most models lack transparency and cannot avoid privacy…
An Explainable and Privacy-Preserving Federated Learning Model for Threat Detection in Cyber-Physical-Social Systems
Grouping Interesting Patterns for Understanding Customer Behaviors
This article presents a highly efficient technique for pattern mining in the realm of customer behavior analysis, termed hybrid clustering patterns for customer behavior analysis (HCP-CBA). It leverages decomposition techniques to uncover relevant patterns by examining correlations among customer transactions within the dataset. Initially, the transaction dataset undergoes decomposition, grouping together transactions exhibiting high correlations…
CDRec-CAS
Recommender Systems (RSs) are a subclass of information filtering systems. RSs assist users in choosing interesting items from an extensive collection of items. This article addresses two research topics in RS, namely cross-domain RSs (CDRSs) and the context-aware RSs (CARSs). CDRSs were developed to improve the quality of recommendations in a target domain using the source domain information. Moreover, CDRSs look to limit the spread of fake info…
Explainable Detection of Fake News on Social Media Using Pyramidal Co-Attention Network
In today’s world, fake news on social media is a universal trend and has severe consequences. There has been a wide variety of countermeasures developed to offset the effect and propagation of Fake News. The most common are linguistic-based techniques, which mostly use deep learning (DL) and natural language processing (NLP). Even government-sponsored organizations spread fake news as a cyberwar strategy. In literature, computational-based detect…
Bert-Based Deceptive Review Detection in Social Media
In recent years, the Internet has facilitated the emergence of social media platforms as significant channels for individuals to express their thoughts and engage in instantaneous interactions. However, the reliance on online reviews has also given rise to deceptive practices, where anonymous spammers generate fake reviews to manipulate the perception of a product. Ensuring the integrity of the online review system requires identifying and mitiga…
Social Web in IoT
Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach u…
A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration
Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high …
Semisupervised Federated Learning for Temporal News Hyperpatism Detection
The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable and accurate content to be disseminated and protecting individuals from the negative effects of false information. In this article, we present a news a…
Advanced Pattern-Mining System for Fake News Analysis
Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news …
Social Media Multiaspect Detection by Using Unsupervised Deep Active Attention
Depression is a severe medical condition that substantially impacts people’s daily lives. Recently, researchers have examined user-generated data from social media platforms to detect and diagnose this mental illness. As a result, in this paper we have focused on phrases used in personal remarks to solve recognizing grief on social media. This research aims to develop generalized attention networks (GATs) that employ masked self-attention layers …
Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance
Robust Collaborative Filtering Recommendation With User-Item-Trust Records
The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first…
Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitte…
Medicine Drug Name Detection Based Object Recognition Using Augmented Reality
Augmented Reality (AR) is an innovation that empowers us in coordinating computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines, providing medication training. AR aids in surgery planning, and patient therapy discloses complex medical circumstances to patients and their family members. With accelerated upgrades in innovation, the ever-increasing number of medical records get accessibl…
Knowledge graph based trajectory outlier detection in sustainable smart cities
Information Granulation-Based Community Detection for Social Networks
Online social networks (OSNs) have become so popular that it has changed the Internet to a more collaborative environment. Now, a third of the world's population participates in OSNs, forming communities, and producing and consuming media in different ways. The recent boom of artificial intelligence technologies provides new opportunities to help improve the processing and mining of social data. In this article, an algorithm that can detect commu…
Cognitive Analytics of Social Media Services for Edge Resource Pre-Allocation in Industrial Manufacturing
With the development of industrial intelligence, the resource requests of various social media services in smart cities are expanding rapidly. For hosting services, the edge computing (EC) platform for its low-latency resource provisioning is fully explored. However, the mapping between edge servers (ESs) and services affects the service latency. Meanwhile, the real-time dynamic distribution of resource requirements also impairs the load balance.…
A Scalable Platform to Collect, Store, Visualize, and Analyze Big Data in Real Time
Twitter has withstood the test of time as a successful social networking platform. In many circles globally, the majority of users choose Twitter when choosing a social media outlet for reliable scientific information and news. However, the Twitter application programming interface (API) limitations do not allow for low-cost data science options for academia. It becomes very expensive for academic researchers to gain the full potential of data an…
Diversified and Scalable Service Recommendation With Accuracy Guarantee
As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user–item rating data decre…
Automated cognitive health assessment in smart homes using machine learning
A Framework-Based Approach to Assessing Mental Health Impacts of the Covid-19 Pandemic on Children and Adolescents
The COVID-19 pandemic has yielded extensive impacts globally in the year of 2020. Although the mental health of children and adolescents may be particularly susceptible to stressors stemming from the pandemic and anti-contagion policies, most ongoing efforts are geared toward curbing the viral spread. In the current perspective, we have identified four domains of factors corresponding to an ecological framework that may directly or indirectly inf…
A Framework for Automatic Categorization of Social Data Into Medical Domains
Nowadays, everyone is surrounded by a large volume of data which is generated from multiple sources. Every day 2.5 quintillion bytes of data are created worldwide. It is a very complicated task to manage and extract useful information from social media platforms. As people are more interested in seeking online health guidance, the medical textual data created by patients, healthcare professionals, and medical staff are expanding fast. In order to…
Why Waldo befriended the dummy? k-Anonymization of social networks with pseudo-nodes
Complexity of social network anonymization
Anonymization and De-anonymization of Social Network Data
A Framework for Automatic Categorization of Social Data Into Medical Domains
Nowadays, everyone is surrounded by a large volume of data which is generated from multiple sources. Every day 2.5 quintillion bytes of data are created worldwide. It is a very complicated task to manage and extract useful information from social media platforms. As people are more interested in seeking online health guidance, the medical textual data created by patients, healthcare professionals, and medical staff are expanding fast. In order to…
Assessing Canadians Health Activity and Nutritional Habits Through Social Media
When conducting data analysis in the twenty-first century, social media is crucial to the analysis due to the ability to provide information on a variety of topics such as health, food, feedback on products, and many others. Presently, users utilize social media to share their daily lifestyles. For example, travel locations, exercises, and food are common subjects of social media posts. By analyzing such information collected from users, health o…
Defensive Modeling of Fake News Through Online Social Networks
Online social networks (OSNs) have become an integral mode of communication among people and even nonhuman scenarios can also be integrated into OSNs. The evergrowing rise in the popularity of OSNs can be attributed to the rapid growth of Internet technology. OSN becomes the easiest way to broadcast media (news/content) over the Internet. In the wake of emerging technologies, there is dire need to develop methodologies, which can minimize the spr…
Information Granulation-Based Community Detection for Social Networks
Online social networks (OSNs) have become so popular that it has changed the Internet to a more collaborative environment. Now, a third of the world's population participates in OSNs, forming communities, and producing and consuming media in different ways. The recent boom of artificial intelligence technologies provides new opportunities to help improve the processing and mining of social data. In this article, an algorithm that can detect commu…
Cognitive Analytics of Social Media Services for Edge Resource Pre-Allocation in Industrial Manufacturing
With the development of industrial intelligence, the resource requests of various social media services in smart cities are expanding rapidly. For hosting services, the edge computing (EC) platform for its low-latency resource provisioning is fully explored. However, the mapping between edge servers (ESs) and services affects the service latency. Meanwhile, the real-time dynamic distribution of resource requirements also impairs the load balance.…
A Scalable Platform to Collect, Store, Visualize, and Analyze Big Data in Real Time
Twitter has withstood the test of time as a successful social networking platform. In many circles globally, the majority of users choose Twitter when choosing a social media outlet for reliable scientific information and news. However, the Twitter application programming interface (API) limitations do not allow for low-cost data science options for academia. It becomes very expensive for academic researchers to gain the full potential of data an…
Diversified and Scalable Service Recommendation With Accuracy Guarantee
As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user–item rating data decre…
Automated cognitive health assessment in smart homes using machine learning
A Framework-Based Approach to Assessing Mental Health Impacts of the Covid-19 Pandemic on Children and Adolescents
The COVID-19 pandemic has yielded extensive impacts globally in the year of 2020. Although the mental health of children and adolescents may be particularly susceptible to stressors stemming from the pandemic and anti-contagion policies, most ongoing efforts are geared toward curbing the viral spread. In the current perspective, we have identified four domains of factors corresponding to an ecological framework that may directly or indirectly inf…
Robust Collaborative Filtering Recommendation With User-Item-Trust Records
The ever-increasing popularity of recommendation systems allows users to find appropriate services without excessive effort. However, due to the unstable and complex network environment, the historical behavior data of users are quite sparse in most cases. The inherent drawbacks render preference prediction infeasible for cold-start users and have become a crucial issue to be resolved in recommendation systems. To deal with the problems, we first…
Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
This research investigates hashtag suggestions in a heterogeneous and huge social network, as well as a cognitive-based deep learning solution based on distributed knowledge graphs. Community detection is first performed to find the connected communities in a vast and heterogeneous social network. The knowledge graph is subsequently generated for each discovered community, with an emphasis on expressing the semantic relationships among the Twitte…
Medicine Drug Name Detection Based Object Recognition Using Augmented Reality
Augmented Reality (AR) is an innovation that empowers us in coordinating computerized data into the client's real-world space. It offers an advanced and progressive methodology for medicines, providing medication training. AR aids in surgery planning, and patient therapy discloses complex medical circumstances to patients and their family members. With accelerated upgrades in innovation, the ever-increasing number of medical records get accessibl…
Knowledge graph based trajectory outlier detection in sustainable smart cities
Semisupervised Federated Learning for Temporal News Hyperpatism Detection
The proliferation of false and erroneous information on the Internet has posed a challenge to the accurate exchange of information. To address this issue, a semisupervised system based on self-embedding has been proposed. This system verifies information before it is shared, allowing only reliable and accurate content to be disseminated and protecting individuals from the negative effects of false information. In this article, we present a news a…
Advanced Pattern-Mining System for Fake News Analysis
Decomposition MapReduce mining for fake news analysis (DMRM-FNA), a novel generic parallel pattern-mining framework, is developed in this article to solve difficulties in social network analysis using big data exploration. The first difficulty faced by existing techniques is the inability to retrieve actionable insights into the structure of fake news data. This can be solved by extracting patterns from fake news and matching them with real news …
Social Media Multiaspect Detection by Using Unsupervised Deep Active Attention
Depression is a severe medical condition that substantially impacts people’s daily lives. Recently, researchers have examined user-generated data from social media platforms to detect and diagnose this mental illness. As a result, in this paper we have focused on phrases used in personal remarks to solve recognizing grief on social media. This research aims to develop generalized attention networks (GATs) that employ masked self-attention layers …
Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance
CDRec-CAS
Recommender Systems (RSs) are a subclass of information filtering systems. RSs assist users in choosing interesting items from an extensive collection of items. This article addresses two research topics in RS, namely cross-domain RSs (CDRSs) and the context-aware RSs (CARSs). CDRSs were developed to improve the quality of recommendations in a target domain using the source domain information. Moreover, CDRSs look to limit the spread of fake info…
Explainable Detection of Fake News on Social Media Using Pyramidal Co-Attention Network
In today’s world, fake news on social media is a universal trend and has severe consequences. There has been a wide variety of countermeasures developed to offset the effect and propagation of Fake News. The most common are linguistic-based techniques, which mostly use deep learning (DL) and natural language processing (NLP). Even government-sponsored organizations spread fake news as a cyberwar strategy. In literature, computational-based detect…
Bert-Based Deceptive Review Detection in Social Media
In recent years, the Internet has facilitated the emergence of social media platforms as significant channels for individuals to express their thoughts and engage in instantaneous interactions. However, the reliance on online reviews has also given rise to deceptive practices, where anonymous spammers generate fake reviews to manipulate the perception of a product. Ensuring the integrity of the online review system requires identifying and mitiga…
Social Web in IoT
Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis of this work where we suggest a novel solution, evolutionary clustering for ontology matching (ECOM), to explore correlations between S-WoT data using clustering and evolutionary computation methodologies. The ECOM approach u…
A DRL-Based Service Offloading Approach Using DAG for Edge Computational Orchestration
Edge infrastructure and Industry 4.0 required services are offered by edge-servers (ESs) with different computation capabilities to run social application's workload based on a leased-price method. The usage of Social Internet of Things (SIoT) applications increases day-to-day, which makes social platforms very popular and simultaneously requires an effective computation system to achieve high service reliability. In this regard, offloading high …
Computer Science (25 works) · Artificial Intelligence (13 works) · World Wide Web (13 works) · Social media (11 works) · Data science (8 works) · Data mining (7 works) · Information retrieval (6 works) · Machine learning (6 works) · Computer security (5 works) · Mathematics (5 works)