Anand Paul

• PhD
• Professor (Associate) at Kyungpook National University

In the realm of smart city development, the integration of intelligent agents has emerged as a pivotal strategy to enhance the efficacy of search methodologies. This study introduces a novel state‐space navigational model employing intelligent agents tailored specifically for fire surveillance in urban environments. Central to this model is the fus...

The growing ubiquity of Internet of Things (IoT) devices within smart homes demands the use of advanced strategies in IoT implementation, with an emphasis on energy efficiency and security. The incorporation of Artificial Intelligence (AI) within the IoT framework improves the overall efficiency of the network. An inefficient mechanism of parent se...

This paper presents an innovative approach to enhance the querying capability of ChatGPT, a conversational artificial intelligence model, by incorporating voice-based interaction and a convolutional neural network (CNN)-based impaired vision detection model. The proposed system aims to improve user experience and accessibility by allowing users to...

The rapid evolution of electronic media in recent decades has exponentially amplified the propagation of fake news, resulting in widespread confusion and misunderstanding among the masses, especially concerning critical topics like the COVID‐19 pandemic. Consequently, detecting fake news on social media has emerged as a prominent area of research,...

The burgeoning role of social network analysis (SNA) in various fields raises complex challenges, particularly in the analysis of dark and dim networks involved in illicit activities. Existing models like the stochastic block model (SBM), exponential graph model (EGM), and latent space model (LSM) are limited in scope, often only suitable for one-m...

This chapter provides a strategic overview of applications in the computer vision domain. We initially introduce the etymology of computer vision, main tasks, key techniques, and algorithms. Traditional feature extraction methods and deep learning techniques, including prominent algorithms like Region-Based Convolutional Neural Network (R-CNN) and...

One of the most appealing multidisciplinary research areas in Artificial Intelligence (AI) is Sentiment Analysis (SA). Due to the intricate and complementary interactions between several modalities, Multimodal Sentiment Analysis (MSA) is an extremely difficult work that has a wide range of applications. In the subject of multimodal sentiment analys...

Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these d...

The advent of Artificial Intelligence (AI) has had a broad impact on life to solve various tasks. Building AI models and integrating them with modern technologies is a central challenge for researchers. These technologies include wearables and implants in living beings, and their use is known as human augmentation, using technology to enhance human...

In An era where technology advances at an unprecedented pace, the healthcare sector stands at the cusp of a transformative revolution. The confluence of distributed Big Data intelligence with instantaneous e-healthcare services heralds a new paradigm, where the boundaries between medicine, artificial intelligence, and data science are blurred, givi...

Intelligent Systems for IoE Based Smart Cities provides simplified information about complexities of cyber physical systems, the Internet of Everything (IoE) and smart city infrastructure. It presents 11 edited chapters that reveal how intelligent systems and IoE are driving the evolution of smart cities, making them more efficient, interconnected,...

In cyber-physical systems (CPS), micromachines are typically deployed across a wide range of applications, including smart industry, smart healthcare, and smart cities. Providing on-premises resources for the storage and processing of huge data collected by such CPS applications is crucial. The cloud provides scalable storage and computation resour...

Extreme Learning Machine (ELM) is a feedforward neural network that utilizes a single hidden layer to effectively tackle the learning speed challenges commonly associated with conventional gradient-based neural networks. ELM has been reported to achieve faster learning rates and better performance than traditional neural networks. However, it is su...

The aim of this paper is to put forth steps to kindle research interest in health care. Health care with artificial intelligence (AI) techniques brings about the e-health system. This research article comprises cancer diagnosis and therapy using AI techniques. Mutations and AI can be applied over the problems to get the end result. The article also...

Artificial Intelligence (AI) approaches allow users to apply algorithms and evaluate data and apply them in various industries, including marine biotechnology. In the field of oceanography, AI techniques are used in remote sensing, maritime transportation, data collection and management, ocean monitoring and predicting the occurrences of various oc...

Accurate estimation of fuel consumption and emissions is crucial for assessing the impact of materials and stringent emission control techniques on climate change, particularly in the transportation industry, which accounts for a significant portion of global greenhouse gases and hazardous pollutants emissions. To address these concerns, the govern...

Recent substantial advancements in computational techniques, particularly in artificial intelligence (AI) and machine learning (ML), have raised the demand for smart self-powered devices. But since energy use is a worldwide issue that needs to be resolved immediately, cutting-edge technology should reduce energy consumption without affecting smart...

This paper introduces DssPyLib, an open-source Python software to compute 2-D electrostatic and magnetostatic fields using the finite element method. With a minimalist interface and non-overlapping simple shapes, the software supports integral and finite element numerical solutions for open boundary problems. The software also provides numerical so...

Hydrotropism is the movement or growth of a plant towards water. It is a type of tropism, or directional growth response, that is triggered by water. Plants are able to detect water through various stimuli, including changes in moisture levels and changes in water potential. The purpose of this study is to provide an overview of how root movement t...

Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades. Convolutional neural networks (CNN) are a significant component of this topic and play a crucial role in the renown of Deep Learning. Inspired by the human vision system, CNN has been appl...

The genes data produced by microarray experiments is complex in terms of dimensions and samples. It consumes a lot of computation power and time when it is processed for a disease analysis while working with an expert system. At the same time, data can help doctors identify a patient’s health condition if it is presented in a meaningful way and pro...

Graph Neural Networks (GNNs) is one of the most essential tools for learning from graph-structured data. A wide range of tasks has demonstrated their usefulness in graph-structured data. Engineering configuration has progressed fundamentally, further developing execution on different forecast errands. Utilizing learnable weight matrices, these neur...

Blockchain may be an optimal solution when a detailed and transparent record of assets is necessary. It is imperative to manage and safeguard digital interactions or maintain a decentralized and shared system of records in applications, such as those used for electricity production, transmission, distribution, and consumption and those used for dat...

The trend of adopting Internet of Things (IoT) in healthcare, smart cities, Industry 4.0, etc. is increasing by means of cloud computing, which provides on-demand storage and computation facilities over the Internet. To meet specific requirements of IoT applications, the cloud has also shifted its service offering platform to its next-generation mo...

The recent advancements and developments in Intelligent Transportation Systems (ITS) lead to the generation of abundant spatio-temporal traffic data. Identifying or understanding the latent patterns present in these spatio-temporal traffic data is very much essential and also challenging due to the fact that there is a chance of obtaining duplicate...

The Republic of Korea is experiencing a demographic crisis with low birth rates and aging population. As the present circumstance represents a developing danger to the supportability of its economy, schooling, accounts, and protection, there is a critical requirement for definite and comprehensive activity. South Korea is one among the world's quic...

A lot of machine learning algorithms, including clustering methods such as K-nearest neighbor (KNN), highly depend on the distance metrics to understand the data pattern well and to make the right decision based on the data. In recent years, studies show that distance metrics can significantly improve the performance of the machine learning or deep...

In recent years, machine learning algorithms have been applied in many real-time applications. Crises in the energy sector are the primary challenges experienced today among all countries across the globe, regardless of their economic status. There is a huge demand to acquire and produce environmentally friendly renewable energy and to distribute a...

Visual inspection is an essential quality control process in industrial businesses. It is usually automated due to its tedious procedure. An automated visual inspection (AVI) attempts to detect items with abnormal patterns based on image data. Recent developments in computer vision models, especially the introduction of deep convolutional neural ne...

The role of demand forecasting in the agriculture management is getting a growing attention. This is due to the fact that scope of visual analytics for forecasting has an extensive range of applications in this domain to benefit the world's economy. Due to the increasing and uncertain demands on the basic grocery needs of the society, implementatio...

Smart City has been an emerging research domain for Government, Businesses, and researchers in the last few years. The Indian government is also interested and investing lots of funds to develop smart cities. These cities are technology-based and require interdisciplinary research and development for successful implementation. Over the last few dec...

The security within autonomous systems (AS)s is one of the important measures to keep network users safe and stable from the various type of Distributed Denial of Service (DDoS) attacks. Similar to the other existing attack types Internet control message protocol (ICMP) based attacks are remained open challenge on the Internet environment. In this...

Consuming Hadoop MapReduce via virtual infrastructure as a service is becoming common practice as cloud service providers (CSP) offers relevant applications and scalable resources. One of the predominant requirements of cloud users is to improve resource utilization in the virtual cluster during the service period. However, it may not be possible w...

Elderly people activity recognition has become a vital necessity in many countries, because most of the elderly people live alone and are vulnerable. Thus, more research to advance in the monitoring systems used to recognize the activities of elderly people is required. Many researchers have proposed different monitoring systems for activity recogn...

The global utilization of electric vehicles (EVs) is exponentially increasing due to the increased availability of cost-efficient EVs and infrastructure managements for the EVs. In spite of the increasing usage of EVs, the problem of EV usage patterns’ analysis and implementing sustainable infrastructure for the EV transportation is still under dev...

Smart grids provide a unique platform to the participants of energy markets to tweak their offerings based on demand-side management. Responding quickly to the needs of the market can help to improve the reliability of the system, as well as the cost of capital investments. Electric load forecasting is important because it is used to make and run d...

A brain tumor is a disorder caused by the growth of abnormal brain cells. The survival rate of a patient affected with a tumor is difficult to determine because they are infrequent and appear in various forms. These tumors can be identified through Magnetic Resonance (MRI) Images, which plays an essential role in determining the tumor site; however...

Emotion recognition (ER) in healthcare has drawn substantial attention owing to recent advancements in machine-learning (ML) and deep-learning (DL) techniques. The ER system, along with a digital twin of a person in real time, will facilitate the monitoring, understanding, and improvement of the physical entity’s capabilities, as well as provide co...

Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, u...

With the increasing pace in the industrial sector, the need for a smart environment is also increasing and the production of industrial products in terms of quality always matters. There is a strong burden on the industrial environment to continue to reduce impulsive downtime, concert deprivation, and safety risks, which needs an efficient solution...

Due to the rapid developments in Intelligent Transportation System (ITS) and increasing trend in the number of vehicles on road, abundant of road traffic data is generated and available. Understanding spatio-temporal traffic patterns from this data is crucial and has been effectively helping in traffic plannings, road constructions, etc. However, u...

Electricity is one of the critical role players to build an economy. Electricity consumption and generation can affect the overall policy of the country. Such importance opens an area for intelligent systems that can provide future insights. Intelligent management for electric power consumption requires future electricity power consumption predicti...

Researchers have thought about clustering approaches that incorporate traditional clustering methods and deep learning techniques. These approaches normally boost the performance of clustering. Getting knowledge from large data-sets is quite an interesting task. In this case, we use some dimensionality reduction and clustering techniques. Spectral...

This is the editorial of the SS entitled ‘'Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation’'.

Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of tr...

Growing vehicular traffic in urban areas creates a mess for authorities to handle city traffic. With the lack of human resources, authorities are moving towards the use of smart and auto-traffic control systems to manage an increasing volume of traffic. Mostly, these systems monitor traffic using street cameras and identify illegal traffic behavior...

Intelligent search techniques and an intelligent agent for smart search are useful in many application domains. We develop a state space navigational model for intelligent agents aimed at industrial surveillance from fire hazards. Our focus is on fire detection using the convolution neural network then proactively search the area which is more like...

The human-in-the-loop cyber-physical system provides numerous solutions for the challenges faced by the doctors or medical practitioners. There is a linear trend of advancement and automation in the medical field for the early diagnosis of several diseases. One of the critical and challenging diseases in the medical field is coma. In the medical re...

In the last decade, technological advancements in the cyber-physical system have set the basis for real-time and context-aware services to ease human lives. The citizens, especially travelers, want to experience a safe, healthy, and timely journey to their destination. Smart and on-ground real-time traffic analysis helps authorities further improve...

In the current scenario, the authors are ranked based on their impact in terms of numbers of citations, total H-index, and the number of papers published. These are also referred to as bibliometric parameters. To rank authors, sometimes two research papers were also considered and compared. There exist many methods that provide authors an index num...

Improving the performance of the MapReduce scheduler is a primary objective, especially in a heterogeneous virtual cloud environment. A map task is assigned with an input split(IS) which consists of one or more data blocks. When a map task is assigned to more than one data block, non-local execution is performed. In classical MapReduce scheduling s...

In the last decade, artificial intelligent systems based on neural networks have gradually become primary source for clinical decision support systems (CDSS) and are being used in diverse areas of medical diagnosis, classification, and prediction. An artificial neural network (ANN) consists of a large number of processing units which performs the c...

A successful implementation of demand response (DR) always depends on proper policy and their empower technologies. This article proposed an intelligent multiagent system to idealize the residential DR in distributed network. In our model, the primary stakeholders (smart homes and retailers) are demonstrated as a multifunctional intelligent agent....

Considering importance of the autonomous driving applications for mobile devices, it is imperative to develop both fast and accurate semantic segmentation models. Thanks to emergence of Deep Learning (DL) techniques, the segmentation models enhanced their accuracy. However, this improved performance of currently popular DL models for self-driving c...

Secret sharing schemes are being widely used to distribute a secret between various participants so that an authorized subset of participants belonging to appropriate access structures can reconstruct this secret. However, a dealer might get corrupted by adversaries and may influence this secret sharing or the reconstruction process. Verifiable sec...

This special issue of the Journal of Intelligent & Fuzzy Systems contains selected articles of fuzzy system for economy back on track.

Convolutional Neural Networks (CNNs) have made a great impact on attaining state‐of‐the‐art results in image task classification. Weight initialization is one of the fundamental steps in formulating a CNN model. It determines the failure or success of the CNN model. In this paper, we conduct a research based on the mathematical background of differ...

Obtaining data with correct labels is crucial to attain the state-of-the-art performance of Convolutional Neural Network (CNN) models. However, labeling datasets is significantly time-consuming and expensive process because it requires expert knowledge in a particular domain. Therefore, real-life datasets often exhibit incorrect labels due to the i...

Blockchain is a new technology that demands more efficient and scalable techniques to incorporate it with business models. Therefore, in this paper, we propose a blockchain-based smart healthcare business model, which keeps customers at the center of business. Our proposed smart healthcare business model can predict customer's status and is able to...

Big data overwhelmed industries and research sectors. Reliable decision making is always a challenging task, which requires cost-effective big data processing tools. Hadoop MapReduce is being used to store and process huge volume of data in a distributed environment. However, due to huge capital investment and lack of expertise to set up an on-prem...

Blockchain expansion is the high priority necessity to improve security. Hacking and other attacks are headway from system innovation and security measures for the cloud architecture. That is why the countermeasure deployed for such attacks should act in an opportune way and ought to be situated as close as possible to attacking device. As an examp...

“We aim to make our readers visualize and learn big data and Hadoop MapReduce from scratch.” There is a lot of Big data, and Hadoop MapReduce content (online lectures, websites) available on the Internet and excellent books are at the counters for intermediate level users to master Hadoop MapReduce. Are they helpful for beginners and non-computer s...

This chapter elaborately discusses MapReduce execution cycle, which is very important to implement scalable algorithms in MapReduce. We have given examples to understand the MapReduce execution sequence step-by-step. Finally, MapReduce weaknesses and solutions are mentioned at the end of the chapter.

This chapter covers single node and multi-node implementation step-by-step with basic wordcount MapReduce job. Some Hadoop administrative commands are given to practice with Hadoop tools.

This chapter discusses the reasons that caused big data and why decision making from digital data is essential. We have compared and contrasted the importance of horizontal scalability over vertical scalability for big data processing. History of Hadoop and its features are mentioned along with different big data processing framework.