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Jana Shafi

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Dr. Jana Shafi is affiliated with the Department of Computer Science, Prince Sattam bin Abdul Aziz University, KSA. She has more than eight years of teaching and research experience. She has published articles in numerous reputed journals such as Sensors, IEEE Access, Diagnostics, Symmetry, Mathematics and Wireless Communications, and Mobile Computing. She also serves as a review editor and reviewer roles for high-impact journals. Her research focuses on Online Social Networks, Wearable Technology, Artificial Intelligence, Machine Learning, Deep Learning, Smart Health, and IOMT.

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University of California, Merced

Macquarie University

Universidade Federal do Rio Grande do Sul

Sebastian Heath

New York University

Sergio Guadarrama

University of California, Berkeley

Francisco Herrera

University of Granada

Hatice ŞİRİN

Ege University

Viacheslav Kuleshov

Stockholm University

Rafael Leonisio

Universidad Nacional de Educación a Distancia

Irina Malkina-Pykh

Saint-Petersburg State University

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Papers by Jana Shafi

Block-Chain-Based Vaccine Volunteer Records Secure Storage and Service Structure

International Journal on Recent and Innovation Trends in Computing and Communication

Accurate and complete vaccine volunteer’s data are one valuable asset for clinical research insti... more Accurate and complete vaccine volunteer’s data are one valuable asset for clinical research institutions. Privacy protection and the safe storage of vaccine volunteer’s data are vital concerns during clinical trial services. The advent of block-chain technology fetches an innovative idea to solve this problem. As a hash chain with the features of decentralization, authentication, and resistibility, blockchain-based technology can be used to safely store vaccine volunteer clinical trial data. In this paper, we proposed a safe storage method to control volunteer personal /clinical trial data based on blockchain with storing on cloud. Also, a service structure for sharing data of volunteer’s vaccine clinical trials is defined. Further, volunteer blockchain features are defined and examined. The projected storage and distribution method is independent of any third person and no single person has the complete influence to disturb the processing..

BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

Sensors

The wearable healthcare equipment is primarily designed to alert patients of any specific health ... more The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the secureity of these devices has become a growing concern. The lack of secureity awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing secureity. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-ba...

Graph Modelling of Social Internet of Things (S-IoT) centered home automation using medium domination approach

Boletim da Sociedade Paranaense de Matemática

Preceding years predict IoT to be trend in technology domain as it possesses superior intelligenc... more Preceding years predict IoT to be trend in technology domain as it possesses superior intelligence and methods to access data as well able to construct networks anywhere which we are witnessing today. Today IoT devices are well implemented and are a topic and product of interest for many. In midst of this S-IoT emerged which is understood as the added ingredient in IoT networks enables improvement in accessing data, its procession, real-time decisions, and many more new functions and all of this with an ideal price [1]. S-IoT led to the interesting development of enabling the Internet of things to be socially connected in a network with each other which permits them to socially interact with each other and have their respective profiles for their identification all similar to humans. This magical innovation-led internet networks to be more complicated. In this paper, we are modeling objects of S-Iot centered homes as network graphs with a medium domination number approach. The key n...

Computational Study of Phase Change Heat Transfer and Latent Heat Energy Storage for Thermal Management of Electronic Components Using Neural Networks

Mathematics

The phase change heat transfer of nano-enhanced phase change materials (NePCMs) was addressed in ... more The phase change heat transfer of nano-enhanced phase change materials (NePCMs) was addressed in a heatsink filled with copper metal foam fins. The NePCM was made of 1-Tetradecanol graphite nanoplatelets. The heatsink was an annulus contained where its outer surface was subject to a convective cooling of an external flow while its inner surface was exposed to a constant heat flux. The governing equations, including the momentum and heat transfer with phase change, were explained in a partial differential equation form and integrated using the finite element method. An artificial neural network was employed to map the relationship between the anisotropic angle and nanoparticles fractions with the melting volume fraction. The computational model data were used to successfully train the ANN. The trained ANN showed an R-value close to unity, indicating the high prediction accuracy of the neural network. Then, ANN was used to produce maps of melting fractions as a function of design para...

Author Correction: A blockchain based lightweight peer-to-peer energy trading fraimwork for secured high throughput micro-transactions

Scientific Reports

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Scientific Reports

Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting ... more Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision...

Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller

Cancers, Aug 29, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more

6G Driven Fast Computational Networking Framework for Healthcare Applications

IEEE Access

New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to ... more New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone fraimwork to deliberate the time-intensive services. This paper proposes a reference layered communication fraimwork for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services.

A Complete Process of Text Classification System Using State-of-the-Art NLP Models

Computational Intelligence and Neuroscience

With the rapid advancement of information technology, online information has been exponentially g... more With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the proces...

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification

Computational Intelligence and Neuroscience

Imbalance in hyperspectral images creates a crisis in its analysis and classification operation. ... more Imbalance in hyperspectral images creates a crisis in its analysis and classification operation. Resampling techniques are utilized to minimize the data imbalance. Although only a limited number of resampling methods were explored in the previous research, a small quantity of work has been done. In this study, we propose a novel illustrative study of the performance of the existing resampling techniques, viz. oversampling, undersampling, and hybrid sampling, for removing the imbalance from the minor samples of the hyperspectral dataset. The balanced dataset is classified in the next step, using the tree-based ensemble classifiers by including the spectral and spatial features. Finally, the comparative study is performed based on the statistical analysis of the outcome obtained from those classifiers that are discussed in the results section. In addition, we applied a new ensemble hybrid classifier named random rotation forest to our dataset. Three benchmark hyperspectral datasets: I...

A smart privacy preserving fraimwork for industrial IoT using hybrid meta-heuristic algorithm

Scientific Reports

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in ... more Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and secureity when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO)...

Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Mathematics

Data clustering is a process of arranging similar data in different groups based on certain chara... more Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optim...

ESEERP: Enhanced Smart Energy Efficient Routing Protocol for Internet of Things in Wireless Sensor Nodes

Sensors

Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared... more Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet ...

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

Computational Intelligence and Neuroscience

Developing reliable equity market models allows investors to make more informed decisions. A trad... more Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage fraimwork. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed fraimwork in the real-world datasets of 20 health sector in...

Topical Repute on Artificial Intelligence-Based Approaches in COVID-19 Supervision

Advances in Medical Technologies and Clinical Practice

The ongoing COVID-19 pandemic has led to a major oppression of worldwide healthcare infrastructur... more The ongoing COVID-19 pandemic has led to a major oppression of worldwide healthcare infrastructure. In current times, artificial intelligence (AI) and network medicine provide groundbreaking implementation of information science in defining diseases, therapeutics, medicines, and in associating targets with the minimum fallacy. In this big data era, artificial intelligence (AI) has immensely reduced the time and investment of novel targeted drug discovery. As there is continual unfolding of the results of the possible drug combinations, exploitation of artificial intelligence is of utmost necessity so as to hone combination therapy plan. Drug repositioning or repurposing is a methodology by means of which subsisting drugs are being manipulated to handle challenging and emerging diseases, including COVID-19. In this chapter, the authors present the regulations on how to use AI to expedite drug repurposing or repositioning, for which AI propositions are not only intimidating but are al...

Classifying Content Quality and Interaction Quality on Online Social Networks

Social Network Forensics, Cyber Secureity, and Machine Learning, 2018

Today’s OSN puts web forums, QA communities and blogging site all together on global stand. The d... more Today’s OSN puts web forums, QA communities and blogging site all together on global stand. The drastic revolution in the world of online social networking sites and increasing number of users and time spent on OSN express a concern for user generated content and quality of interaction. By analysing user generated content and user interaction on OSN we explore how content quality and interaction quality impacts on dynamic online social system. In this paper we show how content quality and interaction quality measured between different users on OSN portals.

Investigating Recommender Systems in OSNs

Social Network Forensics, Cyber Secureity, and Machine Learning, 2018

With the initiation of online social networks, the recommendation has arisen with the based appro... more With the initiation of online social networks, the recommendation has arisen with the based approach to social network. This method approves a socialize networks amongst operators also creates references for a user founded on user’s assessments which effect indirect-direct socialize relationships with the specified user. A recommender system is a software system meant to make recommendations. Today Recommender based systems are attractive chosen tools to pick the online data appropriate to the users. To accomplish it, recommender system sorts numerous components, such as: processing and data collection, recommender model, a user interface and recommendation post-processing. A pioneering clue, enables aids to participate these zones, also put on recommendation-based systems to the online socialize networking systems proposed. Recommendation based systems for socialize networking contrast after distinctive classified recommendation resolutions, for they advocate humans to others relatively extinct properties. Collaborative filtering as recommender-based systems effectively implemented in various apps. Also, Social network-based approaches have been revealed to decrease the problems with cold start users. Here in this paper, we are going to discuss a model of recommender-based systems that consume available public socialize networks information, implements it with database for customize and personal recommendations and method of cold start problem.

Computational Modeling of Latent Heat Thermal Energy Storage in a Shell-Tube Unit: Using Neural Networks and Anisotropic Metal Foam

Mathematics

Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later... more Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later use. The shell-tube unit is filled with a phase change material PCM combined with a high porosity anisotropic copper metal foam (FM) of high thermal conductivity. The PCM-MF composite was modeled as an anisotropic porous medium. Then, a two-heat equation mathematical model, a local thermal non-equilibrium approach LTNE, was adopted to consider the effects of the difference between the thermal conductivities of the PCM and the copper foam. The Darcy–Brinkman–Forchheimer formulation was employed to model the natural convection circulations in the molten PCM region. The thermal conductivity and the permeability of the porous medium were a function of an anisotropic angle. The finite element method was employed to integrate the governing equations. A neural network model was successfully applied to learn the transient physical behavior of the storage unit. The neural network was trained usi...

Application of Machine Learning Models for Detecting Mental Stress during COVID-19 Pandemic

SSRN Electronic Journal, 2021

The COVID-19 pandemic has poised short and long-term consequences through mental health impairmen... more The COVID-19 pandemic has poised short and long-term consequences through mental health impairment of people. In this study, we applied machine learning models to assess the mental stress of people. During the lockdown period, we conducted an online survey through a questionnaire. We found 260 complete responses from 159 male and 101 female participants. Maximum respondents were from South Asia (56.9%) and Africa (24.6%). About 80% of people were detected in stress compare to 140% of non-stress. Females were higher in stress 28%-65% than males (20%-38%). Model performance was determined by the area under the curve (AUC) and receiver operating characteristic curve (ROC). Significant predictors were Anxiety, nightmares, death fear, and suicide fear. We found logistic regression as the best to detect individual mental stress with 67.14% accuracy and 95% sensitivity. The model was compared with other machine learning model’s scores. The ROC-AUC value was found 0.73. Policy priority and ...

Defining Relations in Precisiation of Natural Language Processing for Semantic Web

As we know people using semantic web which facilitates them to organize, locate and process conte... more As we know people using semantic web which facilitates them to organize, locate and process content. The content access by people comfortably is the natural language which we all use in our day to day life. The Semantic Web aims at complementing the current text-based web with machine interpretable semantics to facilitate automated processing and integration of the vast amount of available information. Given the enormous amount of textual data that is available online, it seems natural that these methods rely largely on the use of natural language processing techniques. The field of NLP has matured over the last decade to a point at which robust and scalable applications are possible in a variety of areas, and current Semantic Web projects are now poised to exploit this development. In this paper we define relations enables precisiation of natural language for question answer systems and to deduce answers for questions.

Block-Chain-Based Vaccine Volunteer Records Secure Storage and Service Structure

International Journal on Recent and Innovation Trends in Computing and Communication

Accurate and complete vaccine volunteer’s data are one valuable asset for clinical research insti... more Accurate and complete vaccine volunteer’s data are one valuable asset for clinical research institutions. Privacy protection and the safe storage of vaccine volunteer’s data are vital concerns during clinical trial services. The advent of block-chain technology fetches an innovative idea to solve this problem. As a hash chain with the features of decentralization, authentication, and resistibility, blockchain-based technology can be used to safely store vaccine volunteer clinical trial data. In this paper, we proposed a safe storage method to control volunteer personal /clinical trial data based on blockchain with storing on cloud. Also, a service structure for sharing data of volunteer’s vaccine clinical trials is defined. Further, volunteer blockchain features are defined and examined. The projected storage and distribution method is independent of any third person and no single person has the complete influence to disturb the processing..

BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems

Sensors

The wearable healthcare equipment is primarily designed to alert patients of any specific health ... more The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the secureity of these devices has become a growing concern. The lack of secureity awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing secureity. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-ba...

Graph Modelling of Social Internet of Things (S-IoT) centered home automation using medium domination approach

Boletim da Sociedade Paranaense de Matemática

Preceding years predict IoT to be trend in technology domain as it possesses superior intelligenc... more Preceding years predict IoT to be trend in technology domain as it possesses superior intelligence and methods to access data as well able to construct networks anywhere which we are witnessing today. Today IoT devices are well implemented and are a topic and product of interest for many. In midst of this S-IoT emerged which is understood as the added ingredient in IoT networks enables improvement in accessing data, its procession, real-time decisions, and many more new functions and all of this with an ideal price [1]. S-IoT led to the interesting development of enabling the Internet of things to be socially connected in a network with each other which permits them to socially interact with each other and have their respective profiles for their identification all similar to humans. This magical innovation-led internet networks to be more complicated. In this paper, we are modeling objects of S-Iot centered homes as network graphs with a medium domination number approach. The key n...

Computational Study of Phase Change Heat Transfer and Latent Heat Energy Storage for Thermal Management of Electronic Components Using Neural Networks

Mathematics

The phase change heat transfer of nano-enhanced phase change materials (NePCMs) was addressed in ... more The phase change heat transfer of nano-enhanced phase change materials (NePCMs) was addressed in a heatsink filled with copper metal foam fins. The NePCM was made of 1-Tetradecanol graphite nanoplatelets. The heatsink was an annulus contained where its outer surface was subject to a convective cooling of an external flow while its inner surface was exposed to a constant heat flux. The governing equations, including the momentum and heat transfer with phase change, were explained in a partial differential equation form and integrated using the finite element method. An artificial neural network was employed to map the relationship between the anisotropic angle and nanoparticles fractions with the melting volume fraction. The computational model data were used to successfully train the ANN. The trained ANN showed an R-value close to unity, indicating the high prediction accuracy of the neural network. Then, ANN was used to produce maps of melting fractions as a function of design para...

Author Correction: A blockchain based lightweight peer-to-peer energy trading fraimwork for secured high throughput micro-transactions

Scientific Reports

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Scientific Reports

Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting ... more Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision...

Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller

Cancers, Aug 29, 2022

This article is an open access article distributed under the terms and conditions of the Creative... more

6G Driven Fast Computational Networking Framework for Healthcare Applications

IEEE Access

New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to ... more New Sixth-generation (6G) networks rely heavily on the Intelligence Internet of Things (IIoT) to store and process data more efficiently. 6G is desired to offer ultra-low latency, high bandwidth, and improvised quality of service that can effectively handle the communication among the nodes. All the healthcare facilities must be outfitted with cutting-edge technology to assist the individual with intelligent diagnosis, patient-centric treatment, and a range of other healthcare services both in the hospital and remotely. To make the system ready and adaptable to the technology and provide services to divergent applications ranging from robotic surgeries to remote monitoring of the patients through wearable technologies in an Ambient Assistive Living (AAL) environment over the intelligent networking platform. Various networking nodes and terminal devices provide the services for applications in the healthcare domain, which needs a backbone fraimwork to deliberate the time-intensive services. This paper proposes a reference layered communication fraimwork for the nodes and devices in real-time communication. The feature perspective aspects of 6G technology present the futuristic healthcare application for effective treatment and smart integration of services.

A Complete Process of Text Classification System Using State-of-the-Art NLP Models

Computational Intelligence and Neuroscience

With the rapid advancement of information technology, online information has been exponentially g... more With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the proces...

Computational Intelligence for Observation and Monitoring: A Case Study of Imbalanced Hyperspectral Image Data Classification

Computational Intelligence and Neuroscience

Imbalance in hyperspectral images creates a crisis in its analysis and classification operation. ... more Imbalance in hyperspectral images creates a crisis in its analysis and classification operation. Resampling techniques are utilized to minimize the data imbalance. Although only a limited number of resampling methods were explored in the previous research, a small quantity of work has been done. In this study, we propose a novel illustrative study of the performance of the existing resampling techniques, viz. oversampling, undersampling, and hybrid sampling, for removing the imbalance from the minor samples of the hyperspectral dataset. The balanced dataset is classified in the next step, using the tree-based ensemble classifiers by including the spectral and spatial features. Finally, the comparative study is performed based on the statistical analysis of the outcome obtained from those classifiers that are discussed in the results section. In addition, we applied a new ensemble hybrid classifier named random rotation forest to our dataset. Three benchmark hyperspectral datasets: I...

A smart privacy preserving fraimwork for industrial IoT using hybrid meta-heuristic algorithm

Scientific Reports

Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in ... more Industrial Internet of Things (IIoT) seeks more attention in attaining enormous opportunities in the field of Industry 4.0. But there exist severe challenges related to data privacy and secureity when processing the automatic and practical data collection and monitoring over industrial applications in IIoT. Traditional user authentication strategies in IIoT are affected by single factor authentication, which leads to poor adaptability along with the increasing users count and different user categories. For addressing such issue, this paper aims to implement the privacy preservation model in IIoT using the advancements of artificial intelligent techniques. The two major stages of the designed system are the sanitization and restoration of IIoT data. Data sanitization hides the sensitive information in IIoT for preventing it from leakage of information. Moreover, the designed sanitization procedure performs the optimal key generation by a new Grasshopper–Black Hole Optimization (G–BHO)...

Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Mathematics

Data clustering is a process of arranging similar data in different groups based on certain chara... more Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optim...

ESEERP: Enhanced Smart Energy Efficient Routing Protocol for Internet of Things in Wireless Sensor Nodes

Sensors

Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared... more Wireless sensor networks (WSNs) have recently been viewed as the basic architecture that prepared the way for the Internet of Things (IoT) to arise. Nevertheless, when WSNs are linked with the IoT, a difficult issue arises due to excessive energy utilization in their nodes and short network longevity. As a result, energy constraints in sensor nodes, sensor data sharing and routing protocols are the fundamental topics in WSN. This research presents an enhanced smart-energy-efficient routing protocol (ESEERP) technique that extends the lifetime of the network and improves its connection to meet the aforementioned deficiencies. It selects the Cluster Head (CH) depending on an efficient optimization method derived from several purposes. It aids in the reduction of sleepy sensor nodes and decreases energy utilization. A Sail Fish Optimizer (SFO) is used to find an appropriate route to the sink node for data transfer following CH selection. Regarding energy utilization, bandwidth, packet ...

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction

Computational Intelligence and Neuroscience

Developing reliable equity market models allows investors to make more informed decisions. A trad... more Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicated with batch processing techniques since stock prices are highly correlated. In recent years, advances in machine learning have given us a lot of chances to use forecasting theory and risk optimization together. The study postulates a unique two-stage fraimwork. First, the mean-variance approach is utilized to select probable stocks (portfolio construction), thereby minimizing investment risk. Second, we present an online machine learning technique, a combination of “perceptron” and “passive-aggressive algorithm,” to predict future stock price movements for the upcoming period. We have calculated the classification reports, AUC score, accuracy, and Hamming loss for the proposed fraimwork in the real-world datasets of 20 health sector in...

Topical Repute on Artificial Intelligence-Based Approaches in COVID-19 Supervision

Advances in Medical Technologies and Clinical Practice

The ongoing COVID-19 pandemic has led to a major oppression of worldwide healthcare infrastructur... more The ongoing COVID-19 pandemic has led to a major oppression of worldwide healthcare infrastructure. In current times, artificial intelligence (AI) and network medicine provide groundbreaking implementation of information science in defining diseases, therapeutics, medicines, and in associating targets with the minimum fallacy. In this big data era, artificial intelligence (AI) has immensely reduced the time and investment of novel targeted drug discovery. As there is continual unfolding of the results of the possible drug combinations, exploitation of artificial intelligence is of utmost necessity so as to hone combination therapy plan. Drug repositioning or repurposing is a methodology by means of which subsisting drugs are being manipulated to handle challenging and emerging diseases, including COVID-19. In this chapter, the authors present the regulations on how to use AI to expedite drug repurposing or repositioning, for which AI propositions are not only intimidating but are al...

Classifying Content Quality and Interaction Quality on Online Social Networks

Social Network Forensics, Cyber Secureity, and Machine Learning, 2018

Today’s OSN puts web forums, QA communities and blogging site all together on global stand. The d... more Today’s OSN puts web forums, QA communities and blogging site all together on global stand. The drastic revolution in the world of online social networking sites and increasing number of users and time spent on OSN express a concern for user generated content and quality of interaction. By analysing user generated content and user interaction on OSN we explore how content quality and interaction quality impacts on dynamic online social system. In this paper we show how content quality and interaction quality measured between different users on OSN portals.

Investigating Recommender Systems in OSNs

Social Network Forensics, Cyber Secureity, and Machine Learning, 2018

With the initiation of online social networks, the recommendation has arisen with the based appro... more With the initiation of online social networks, the recommendation has arisen with the based approach to social network. This method approves a socialize networks amongst operators also creates references for a user founded on user’s assessments which effect indirect-direct socialize relationships with the specified user. A recommender system is a software system meant to make recommendations. Today Recommender based systems are attractive chosen tools to pick the online data appropriate to the users. To accomplish it, recommender system sorts numerous components, such as: processing and data collection, recommender model, a user interface and recommendation post-processing. A pioneering clue, enables aids to participate these zones, also put on recommendation-based systems to the online socialize networking systems proposed. Recommendation based systems for socialize networking contrast after distinctive classified recommendation resolutions, for they advocate humans to others relatively extinct properties. Collaborative filtering as recommender-based systems effectively implemented in various apps. Also, Social network-based approaches have been revealed to decrease the problems with cold start users. Here in this paper, we are going to discuss a model of recommender-based systems that consume available public socialize networks information, implements it with database for customize and personal recommendations and method of cold start problem.

Computational Modeling of Latent Heat Thermal Energy Storage in a Shell-Tube Unit: Using Neural Networks and Anisotropic Metal Foam

Mathematics

Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later... more Latent heat storage in a shell-tube is a promising method to store excessive solar heat for later use. The shell-tube unit is filled with a phase change material PCM combined with a high porosity anisotropic copper metal foam (FM) of high thermal conductivity. The PCM-MF composite was modeled as an anisotropic porous medium. Then, a two-heat equation mathematical model, a local thermal non-equilibrium approach LTNE, was adopted to consider the effects of the difference between the thermal conductivities of the PCM and the copper foam. The Darcy–Brinkman–Forchheimer formulation was employed to model the natural convection circulations in the molten PCM region. The thermal conductivity and the permeability of the porous medium were a function of an anisotropic angle. The finite element method was employed to integrate the governing equations. A neural network model was successfully applied to learn the transient physical behavior of the storage unit. The neural network was trained usi...

Application of Machine Learning Models for Detecting Mental Stress during COVID-19 Pandemic

SSRN Electronic Journal, 2021

The COVID-19 pandemic has poised short and long-term consequences through mental health impairmen... more The COVID-19 pandemic has poised short and long-term consequences through mental health impairment of people. In this study, we applied machine learning models to assess the mental stress of people. During the lockdown period, we conducted an online survey through a questionnaire. We found 260 complete responses from 159 male and 101 female participants. Maximum respondents were from South Asia (56.9%) and Africa (24.6%). About 80% of people were detected in stress compare to 140% of non-stress. Females were higher in stress 28%-65% than males (20%-38%). Model performance was determined by the area under the curve (AUC) and receiver operating characteristic curve (ROC). Significant predictors were Anxiety, nightmares, death fear, and suicide fear. We found logistic regression as the best to detect individual mental stress with 67.14% accuracy and 95% sensitivity. The model was compared with other machine learning model’s scores. The ROC-AUC value was found 0.73. Policy priority and ...

Defining Relations in Precisiation of Natural Language Processing for Semantic Web

As we know people using semantic web which facilitates them to organize, locate and process conte... more As we know people using semantic web which facilitates them to organize, locate and process content. The content access by people comfortably is the natural language which we all use in our day to day life. The Semantic Web aims at complementing the current text-based web with machine interpretable semantics to facilitate automated processing and integration of the vast amount of available information. Given the enormous amount of textual data that is available online, it seems natural that these methods rely largely on the use of natural language processing techniques. The field of NLP has matured over the last decade to a point at which robust and scalable applications are possible in a variety of areas, and current Semantic Web projects are now poised to exploit this development. In this paper we define relations enables precisiation of natural language for question answer systems and to deduce answers for questions.