Calls for Papers (special): International Journal of Data Warehousing and Mining (IJDWM)


Special Issue On: Data Mining with Knowledge

Submission Due Date
8/23/2022

Guest Editors
Dr. Hongzhi Wang, Harbin Institute of Technology, China (wangzh@hit.edu.cn)
Dr. Aya Elfatyany, Menoufia University, Egypt (usually2011@yahoo.com)
Dr. Dan Lu, Harbin Engineering University, China (ludan@hrbeu.edu.cn)

Introduction
Data Mining provides a useful insight on finding hidden valuable information and helping data-based decision making. Data mining has made a profound impact on a variety of data-rich domains such as market basket analysis, Education, Manufacturing Engineering, CRM and Anomaly Detection, some of which exist domain knowledge that could have a bearing on the data mining effectiveness. Domain knowledge consists of information about the data that is already available either through some other discovery process or from a domain expert. This journal mainly focus on the data mining process with domain knowledge.

Objective
The goal of this Special Issue aims to provide an unique opportunity to present the work on state-of-the-art of data mining algorithms with domain knowledge in the area of big data processing. This will provide a snapshot of the latest advances in the contribution of domain knowledge in big data mining applications to solve problems in practical applications. The selected papers will be beneficial to both academia and industry, for delivering the significant research outcomes and inspiring new real-world applications.

Recommended Topics
The topics of interest include, but are not limited to:
  • Novel applications for big data Analytics.
  • Data Mining in Big Data
  • Social Network Analysis and Web Mining
  • Analysis and Mining of Big Text Data
  • Recommender System, Graph Mining, Data Stream mining and Time Series Analysis
  • Anomaly Detection with domain knowledge
  • Association Rule Learning in Data Mining
  • Data Mining on Social Business


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Data Mining with Knowledge on or before August 23rd, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Hongzhi Wang
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-mail: wangzh@hit.edu.cn

Special Issue On: New Frontiers in Hypertext and Data Science for Web 4.0

Submission Due Date
9/8/2022

Guest Editors
Dr. Jia-Bao Liu, Anhui Jianzhu University, China (liujiabao@ieee.org)
Dr. Muhammad Javaid, University of Management and Technology, Pakistan (muhammad.javaid@umt.edu.pk)
Dr. Mohammad Reza Farahani, Iran University of Science and Technology, Iran (mrfarahani88@gmail.com)

Introduction

Web 4.0 is perhaps a symbiotic interconnection among humans and robots. Web 4.0 would enable more sophisticated interactions, including mind driven functionalities. Moreover, machines could become smart enough to understand content online, respond by processing it, and decide which one to run next and render websites faster, better, with more authoritative interactions. Web 4.0 establishes a critical engagement threshold within internet-based networks that provide worldwide openness, administration, dissemination, engagement, and cooperation in vital areas like industry, politics, and social issues. Web 4.0, or webOS, would work as a middleware that would eventually take on the characteristics of an operating system.


Web 4.0 will function similarly towards the human mind, implying a vast web of extremely intelligent interconnections. Furthermore, web 4.0 is evolving toward the convergence of artificial intelligence to be an intellectual web. The next generation of web technology will be dominated by AR technology and data science. It is believed that this is an era where each individual has an internal digital model and, therefore, will engage with different platforms, such as intelligent devices, continuously. Additionally, there is a relatively apocalyptic view of the Web in the coming years, featuring maximum control over the knowledge that will touch the digital experience and the real world.



Objective

Data science seems to impact every aspect of web 4.0 significantly. Responsive web design is perhaps the most adversely impacted of all disciplines. As previously noted, web design has been heavily reliant on survey data, focus group comments, and preconceptions throughout these periods. However, with the advent of data science, web design is experiencing a shift. Developers, therefore, have relevant and trustworthy information, which enables organizations to understand the end user behaviour, customers' needs, and market dynamics. Machine learning methods enable us to maximize the value of the data pool, thereby revealing critical insights. The insights mentioned above are primarily focused on the consumer experience while interacting with an app. further, it must help decide which characteristics our clients prefer. Additionally, it would provide insight towards whatever requires to be altered. Machine learning algorithms are competent in inferring all fundamentals from developing patterns and behaviours and will automatically send version updates as applicable for hypertext in web 4.0.


The fourth-generation Web, entitled as intelligent, could be more intuitive, undetectable, pervasive, and omnipresent due to its symbiotic relationship between interconnected devices and customers' surroundings. These devices and Web 4.0 will continue to improve their understanding of natural language and its ability to monitor customer behaviours according to customer requirements, often without customer interaction or required to transit across a digital dashboard. Thus, Web 4.0 is a critical component for the virtualization of the entire globe, wherein robots and humans would interact more seamlessly.



Recommended Topics
  • Urban Data Science for Knowledge management and engineering in Web 4.0
  • Advance machine learning for acquiring information for Intelligence in Web 4.0 hypertext
  • AI-based data-driven decision's support systems for Web 4.0
  • Advance data science technique for Visual Analytics and hypertext in Web 4.0
  • Data mining in Web 4.0 for knowledge and insight extraction
  • Adaptive hypertext infrastructures and frameworks for Web 4.0
  • Enhanced in Human Computation & Crowdsourcing in Web 4.0
  • Advanced-Data Analysis based Social Network Analysis in Web 4.0
  • Semantic web for Hypermedia information retrieval in Web 4.0
  • Link analysis and link prediction for Web 4.0


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on New Frontiers in Hypertext and Data Science for Web 4.0 on or before September 9th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Jia-Bao Liu
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-Mail: liujiabao@ieee.org

Special Issue On: Advances in Database Management Systems for Business Data Visualization

Submission Due Date
9/16/2022

Guest Editors
Dr. Dinh Tran Ngoc Huy, International University of Japan, Japan (dtnhuy@ieee.org)
Dr. Esra SİPAHİ DÖNGÜL, Aksaray Üniversitesi, Turkey (dresrasipahi@gmail.com)
Dr. Pham Van Tuan, National Economics University, Vietnam (phamvantuan@neu.edu.vn)
Dr. Hoang Thanh Hanh, Academy of Policy and Development, Vietnam (hoangthanhhanh@apd.edu.vn)

Introduction
Today’s developing digital economy has made companies access more data than ever before. These data are foundation for intelligence business decisions. Also, companies must invest in data management systems to ensure employees have the correct data for decision-making. The purpose of a database is to assist, organize and store large volumes of data. The database environment also stores, manipulates and manage data to format names of fields, record and file structures. It may also include Textual, Numerical and Binary data. However, to get a clear idea of business processes, a more natural human mind is needed to comprehend them. Further, implementing proper database management helps to increase organizational accessibility of data that allows end-users to share the information quickly and effectively across the organization. Improving dynamic data base management and business data visualization has a more critical role to play. Because, visualizations unveil hidden patterns and give new insights into data patterns and structures. Moreover, visualization is a pictorial representation of data in charts, graphs, tables, infographics, maps, etc. In short, an advanced database management system for business enterprises uses strategies and technologies for analysing current and historical data to improve strategic decision-making to provide a competitive advantage.

Objective
Advanced data management systems like Microsoft Access Oracle, Microsoft SQL Server, Teradata, IBM DB2, Informix, SAP ASE, Amazon’s Simple DB and many others enables companies to make use of predictive analytics for real-time decisions making. It also includes AI-powered automation and graph database analytics, to support transactional workloads play essential roles in an organization’s data environment, making critical functions easier and less time-intensive. The data management performs the following tasks like data modelling, security governance, warehousing, extracting, transforming and loading data. It also helps to clearly define business objectives and track relevant information preventing data management software from becoming overcrowded and unmanageable. It also focuses on parameters like data quality, allowing people to access data and prioritize data security. It enables business organizations to find quick solutions to database queries, thus provide faster and more accurate data access. Further implementing data management system for Business Data visualization shows data in visual or graphical format enabling decision-makers to see analytics and grasp complex concepts. It also detects patterns, trends and brings correlations between different data sets thereby making analysing process simple. Hence, an effective data management system provides unique solutions by integrating metadata and management processes.

However, database management has some drawbacks in handling extensive data and managing time-sequenced data because of an appropriate data model. Despite these factors, advances in data management in business processes increase visibility, reliability, security, and scalability in data. This special issue explores the current opportunities and future advancements in data base management system for business data visualisation. It presents a unique opportunity for researchers to present innovative research contributions in this context collaboratively.



Recommended Topics
  • Multidimensional database modelling for business data visualisation
  • The object-oriented database system for business data visualisation
  • Faster querying in Data base management system for business data visualisation
  • Scalable and efficient data base system for business data visualisation.
  • Provenance of Interactive business data visualizations in database management systems
  • Interactive business data Visualization with advanced data base management systems.
  • Query processing and optimization in data base management system for business data visualisation.
  • Data modelling, visualization, personalization, in an advanced data management system for business data visualisation
  • Multi-databases and database Federation for business data visualisation.
  • Temporal, Spatial, and High Dimensional Databases management system for business data visualisation.


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Advances in Database Management Systems for Business Data Visualization on or before September 16th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Dinh Tran Ngoc Huy
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-Mail: dtnhuy@ieee.org

Special Issue On: HPC-Big Data Convergence, Challenges and Potentials

Submission Due Date
10/1/2022

Guest Editors
Dr. Manjit Kaur, Gwangju Institute of Science and Technology, Republic of Korea.
Dr. Raman Singh, The University of the West of Scotland, United Kingdom.
Hasséne Gritli, University of Carthage, Tunisia.

Introduction
The capability to analyses data by performing complicated calculations at incredibly fast speeds is considered High-Performance Computing (HPC). To experience things, a 3 GHz Central processing unit on a device computer can complete roughly 3 billion computations each sec. Although this is far quicker than any humans, it pales compared to High-performance computing systems that can execute quadrillions of transactions per second. Innovative data storage paradigms are being carved out by High-Performance Computing and Big Data technologies. The always scale of computing and datasets absorbed and generated by massive-scale systems necessitates this. The adoption of bursting buffers’ modern I/O frameworks for High-Performance Computing and the effectiveness of important stored or blocking storage solutions across Clouds demonstrates a need. Big data is a field concerned with analyzing, methodically extracting information through, or perhaps dealing with data volumes that are too massive or complicated for typical data-handling applications and services to comprehend.

Objective
This Special Issue covers the deployment of high-performance computing in the process of big data convergence and the real-time issues and feasible benefits. This motivates big data specialists and computer enthusiasts to research trailing areas and submit their papers to overcome the present limits. It offers a consistent foundation for future technologies.

Recommended Topics
  • Hardtop in addressing of Big data: Challenges and potentials
  • Hybrid technology for HDFS and HPC clusters with architecture
  • Big data in smart cities: Research and challenges
  • Big Data for Machine Learning and Application
  • High-performance big data and intelligent systems
  • Efficient and performance storage systems: Challenges and Opportunities
  • Distributed Intelligent for HPC Unmanned Mobile
  • Enable Drivers supercomputer using HPC virtual clusters
  • Big data analytics for Healthcare
  • Framework for Big data for smart buildings
  • Advanced tools for Big Data analytics in ML
  • ML and Human-computer interaction
  • Big data in Blockchain and Security
  • IoT based big data


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on HPC-Big Data Convergence, Challenges and Potentials on or before October 1st, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Manjit Kaur.
Dr. Raman Singh.
Dr. Hasséne Gritli.
Guest Editors
International Journal of Data Warehousing and Mining (IJDWM)
Emails: manjit@ieee.org; Raman.Singh@uws.ac.uk; grhass@yahoo.fr

Special Issue On: Advancements in Web Data Mining and Hidden Challenges

Submission Due Date
10/30/2022

Guest Editors
Dr. Dilbag Singh, School of Electrical Engineering and Computer Science, Republic of Korea.
Dr. Robertas Damaševičius, Silesian University of Technology, Poland.
Dr. Vijay Kumar, National Institute of Technology, Hamirpur, India.

Introduction
Nowadays, web information delighted an essential value of consideration in the application also the community as a complex in current years, due to the extensive accessibility of enormous quantities of data and the immediate demand for returning such information data into valuable data and experience. The intelligence and information attained can be employed for network systems varying from market intelligence, fraud disclosure, and several searches.Application database operations have prospered with dimensional, temporary, multimedia systems, sensing elements, engineering and experimental databases, information bases, and position information posts. Some issues associated with the administration, modification and splitting of data have been analysed widely. Various database technology and network-based global data systems like the World Wide Web must also emerge and perform an influential position in the data industry.

Objective
This Special Issue will focus on recent advances in networking technologies approach a significant analysis in web technology. To execute it possible to use the extensive knowledge obtainable on the web entirely, each one must overwhelm many mining difficulties to make the network more productive, friendlier, and further intelligent support.

Recommended Topics
  • The new characters and several familiarities of data mining
  • The three-dimensional range for web data mining information
  • A multidisciplinary application in web data mining
  • The role of electronically connected conditions for power-boosting
  • The causes of the inadequacy of data, difficulty, and approach noise
  • The Method recounted difficulties in web data mining
  • Computing up for powerful-speed data currents and also unique dimensional data
  • Constructing a consolidation approach of web data mining
  • Data mining also with privacy and security preservation
  • Role of data mining in situation surveillance of high Voltage electrically powered apparatus
  • The role of web data mining for hidden obstacles
  • The challenges of data mining interdepartmental data and classified data mining


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Advancements in Web Data Mining and Hidden Challenges on or before July 1st, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Dilbag Singh.
Dr. Robertas Damaševičius.
Dr. Vijay Kumar.
Guest Editors
International Journal of Data Warehousing and Mining (IJDWM)
Emails: Dilbagsingh@gist.ac.kr; robertas.damasevicius@polsl.pl; vijaykumarchahar@nith.ac.in

Special Issue On: Semantic Medical Data Mining in Health Informatics

Submission Due Date
11/4/2022

Guest Editors
Dr. Fasee Ullah, University of Macau, Pakistan.
Dr. Arafat Al-Dhaqm, Universiti Teknologi Malaysia, Malaysia.
Dr. Masood Ahmad, Abdul Wali Khan University, Pakistan.

Introduction
Semantic interactivity in health informatics is primarily significant as all certain several models of data require to be frequently switched between intelligence systems. The application of semantic sources techniques is crucial for scoring semantic compatibility and thus developing intelligence in health informatics and eventually profiting health care. The portrayal of complicated health information requires sufficient organizing to promote accurate apportionment and rendition. More precisely, encoding and analysis systems are significant tools for the unambiguous classification of medical data abstraction at the method of care during the transmission of health services. The prompt development of health informatics has improved passage to medical informatics and authorizes the amplification of knowledge in healthcare informatics systems.

Objective
This Special Issue intends to increase consciousness for the necessity to support semantic compatibility in health informatics and thus to combine well-organized and unorganized information and stimulate the analysis communal to improve methods and device-based measures to promote semantic data in health informatics, and data interpretation and evaluation in this sphere of healthcare.

Recommended Topics
  • Prejudice in the clinical semantic application
  • FAIRness (Findability, Approachability, Interoperability, and Recycling) in health data governance
  • Accountable and decipherable analytic data in health data care management
  • The role of semantic management technology in covid-19 pandemic
  • The role of machine learning and artificial intelligence in healthcare data management
  • Data graph structure on health database
  • Blockchain-based solvents for clinical data processing
  • Semantic combination of different medications on electronic data sources
  • Sensing element in data assimilation with health care data
  • Web-scale & cloud-based health data management systems
  • Artificial intelligence and text mining technology for health care
  • Quality of data, outlining, and challenges on health care
  • Artificial intelligence and text mining technology for health care
  • Semantic ancestry and explanation of biomedical data


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Semantic Medical Data Mining in Health Informatics on or before November 4th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Fasee Ullah.
Dr. Arafat Al-Dhaqm.
Dr. Masood Ahmad.
Guest Editors
International Journal of Data Warehousing and Mining (IJDWM)
Emails: faseekhan@ieee.org; mrarafat1@utm.my; Masood@awkum.edu.pk

Special Issue On: APPLICATION OF ARTIFICIAL INTELLIGENCE IN DIAGNOSIS AND PREDICTION OF DISEASES

Submission Due Date
11/30/2022

Guest Editors
Dr. Manju Bala, Khalsa College of Engineering and Technology Amritsar, Punjab (drmanju571@gmail.com)
Dr. Sandeep Kumar Sood, National Institute of Technology, Kurukshetra, Haryana (sandeepsood@nitkkr.ac.in)
Dr. Arshpreet Kaur, Alliance University, Bangalore, India (arshpreet@nitdelhi.ac.in)
Dr. Kumar Shashvat, Alliance College of Engineering and Design, Alliance University, Bangalore, India (shashvat.sharma13@gmail.com)


Introduction

Diagnosis of any disease is perplexing, since numerous cyphers and indications are nonspecific, and can only be undertaken by registered and licensed health professionals. Accurate diagnosis of any disease needs an expert doctor. Diagnosis is the first and very crucial step. Earlier the diagnosis is established more affective the treatment can be given and better are the chances of a successful treatment. Diagnosis can be a time consuming and costly apart from being one from being the basic steps towards treatment. Artificial intelligence has played major roles in reinvention in every field. It has been used in diagnosis and is filling gaps for many others. Machine learning contributes provides remunerations over conventional strategies for examination and settling on clinical choices. Various software has been build that permits Pathologists to make exact analyses. With expanded precision in the analysis of malignancy patients, exact Cancer Diagnosis, early diagnosis of fatal blood Diseases. It contributes as it acts as support system and provides required assistance with regards to diagnosing conceivably at a beginning phase. While for some disease diagnostic is a challenge while for others learning about their spread and prediction and forecasting the number of cases in future for a disease, still remains problem to be conquered. Vector-borne diseases are the most protuberant intimidations to human health. They are transferred to the human population by infected insects or by unswerving transmission amid humans. Machine learning and deep learning models have given valuable input in designing representative epidemiological models incorporating environmental features that show a close relationship with the epidemic process observed in the human population. These machine learning models have given a better understanding of consequences a disease may leave.

Among other applications the contribution of artificial intelligence in time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease such as can be said for the novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, await impending risk in attempting the epidemic. Different forecasting models are being used to predict future cases of disease such as COVID-19.



Objective

The objective of this special issue is to prediction of infectious diseases that established more affective the treatment can be given and better are the chances of a successful treatment. Diagnosis can be a time consuming and costly apart from being one from being the basic steps towards treatment. Artificial intelligence has played major roles in reinvention in every field. It has been used in diagnosis and is filling gaps for many others. Machine learning contributes provides remunerations over conventional strategies for examination and settling on clinical choices. Various software has been built that permits Pathologists to make exact analyses. With expanded precision in the analysis of malignancy patients, exact Cancer Diagnosis, early diagnosis of fatal blood Diseases. It contributes as it acts as support system and provides required assistance with regards to diagnosing conceivably at a beginning phase. While for some disease diagnostic is a challenge while for others learning about their spread and prediction and forecasting the number of cases in future for a disease, remains problem to be conquered. Vector-borne diseases are the most protuberant intimidations to human health. They are transferred to the human population by infected insects or by unswerving transmission amid humans. Machine learning and deep learning models have given valuable input in designing representative epidemiological models incorporating environmental features that show a close relationship with the epidemic process observed in the human population. These machine learning models have given a better understanding of consequences a disease may leave.

Among other applications the contribution of artificial intelligence in time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease such as can be said for the novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, await impending risk in attempting the epidemic. Different forecasting models are being used to predict future cases of disease such as COVID-19.



Recommended Topics
  • Artificial Intelligence in Healthcare Diagnosis
  • Deep Learning for Diagnosis and Prediction
  • Predicting Trends in Epidemiological Disease
  • Deep learning in Medical Imaging
  • Time series model for Viral Disease
  • Devices for Healthcare/ Wearable Devices
  • Social Network
  • Vector Borne Diseases
  • Epidemiological Models
  • Data Analysis and preprocessing


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on APPLICATION OF ARTIFICIAL INTELLIGENCE IN DIAGNOSIS AND PREDICTION OF DISEASES on or before November 30th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Kumar Shashvat
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-Mail: shashvat.sharma13@gmail.com

Special Issue On: Enhancing Healthcare Systems With Big Data Models And Algorithms

Submission Due Date
12/12/2022

Guest Editors
Santhosh Kumar, Guru Nanak Institute of Technology, India
BalaAnand Muthu, Adhiyamaan College of Engineering, India
Imran Shafique Ansari, University of Glasgow, United Kingdom

Introduction
This special issue addresses the need for big data algorithms and various roles and applications in healthcare. It focuses on the challenges faced by the healthcare industry for its successful implementation. Researchers and practitioners are welcome to provide innovative solutions to the same problem.

Objective
Healthcare is amongst the most noteworthy domains where extensive data analysis has a significant impact. Large electronic health databases, which are challenging to handle with traditional technologies and tools, are the source of Big Data in healthcare. The usage of antiquated data management approaches and techniques prevents all of this data from being productively leveraged. Healthcare informatics advances big data analytics technologies by providing new issues in terms of data knowledge structure, database architecture, data retrieval, and medical decision assistance.

Recommended Topics
  • Recent trends in big data algorithms and models in the healthcare system.
  • Applications of big data algorithms and models in medical imaging.
  • Applications of big data algorithms and models in healthcare management.
  • Emerging applications and future research of big data algorithms in the healthcare system.
  • Big data algorithms and models in the diagnosis and prevention of diseases.
  • Real-time alerting in healthcare management using big data models.
  • Effective treatment of diseases based on applications of big data models.
  • Challenges in implementing big data models in the healthcare system.
  • Big data algorithms and models are transforming the health care industry.
  • Impact of Big data and models in the discovery of new drugs.
  • Role of big data in improving decentalized hospital operations.
  • Big data models revolutionizing the medical field.
  • Big data models and algorithms in precision medicine.
  • Big data algorithms and models in context with the IoT in tracking health issues.


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Enhancing Healthcare Systems With Big Data Models And Algorithms on or before December 12, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

This is a full open access journal. Authors of manuscripts that are accepted to publish in this special issue will be expected to pay the article processing charge.



Open Access Resources:

All inquiries should be directed to the attention of:
Santhosh Kumar
BalaAnand Muthu
Imran Shafique Ansari
Guest Editor:
International Journal of Data Warehousing and Mining (IJDWM)
Email: bsanthosh.csegnit@gniindia.org; balaanand@ieee.org; Imran.Ansari@glasgow.ac.uk

Special Issue On: User-Centric Opinion Mining and Sentiment Analysis for Next Generation Intelligent Information Systems

Submission Due Date
12/15/2022

Guest Editors
Dr. Manoj Kumar, University of Petroleum and Energy Studies, Dehradun, India (m.kumar@ddn.upes.ac.in)
Dr. Octavio Loyola-González, Altair Management Consultants Corporation, Waltham, USA (olg@altair.consulting)
Dr.Xiaochun Cheng, Middlesex University, London, U.K (X.Cheng@mdx.ac.uk)

Introduction
In recent times, the amount of information and data generated both online and offline is expanding daily. Almost any sector and organisation uses information extraction solutions and technologies domestically and globally, and billions of data centres are now devoted to recommendation algorithms, executing information retrieval and marketing. Human-machine interaction has become feasible with modern computing research and innovation. Data can be accessed in numerous formats such as big data, semi-structured, unstructured, and structured, making it challenging to handle the complexity of information and data. Information extraction from these many data sources and formats is difficult to control. Unlike practical Information retrieval algorithms, we wouldn't have web search, social media, or decision support systems.

Next-Generation intelligent information systems augment new technologies, including 5G technologies, Artificial intelligence, edge intelligence, blockchain, and virtual reality. Machine Learning and Deep learning provide autonomous services and intelligent applications. Next-Generation intelligent information systems may benefit from the use of Deep Reinforcement Learning and Federated Learning. Hence, next-generation intelligent information systems need to cooperate through retrieval, manipulation, intelligent behaviour, discovery and problem-solving with a wide variety of user-centric approaches for knowledge extraction. Also, it requires a mechanism to handle massive volumes of multimedia knowledge and information and reason about data in uncertain situations. User-centric opinion mining and sentiment analysis would be prominent approaches to deal with shortfalls in next-generation information systems.

Sentiment analysis is a kind of NLP technology that analyses emotional content in documents. This is a standard way to collect and categorise client feedback. Data mining, machine learning, and AI are used to mine text for User-Centric Opinion Mining and Sentiment Analysis. Moreover, sentiment analysis is classified as fine-grained sentiment analysis provides more precise polarity by categorising it from very positive to very negative; further emotion detection also detects particular emotions instead of positive or negative. Besides, aspect-based analysis collects negative or positive components, and Intent-based analysis distinguishes between acts and opinions. Hence, next-generation intelligent information systems require user-centric opinion mining and sentiment analysis for automatic systems to learn from data using machine learning methods. Rule-based systems execute sentiment analysis using predetermined, lexicon-based rules. Hybrid sentiment analysis refers to a technique that combines the two approaches, such as rule-based systems and lexicon-based rules. Additionally, manual analysis is impractical, with data rising by the day in next-generation intelligent information systems. Online polling is a kind of opinion mining; with the help of the NLP approach, it instantly grasps the user's sentiments and emotions.



Objective
Hybrid sentiment analysis refers to a technique that combines the two approaches, such as rule-based systems and lexicon-based rules. Additionally, manual analysis is impractical, with data rising by the day in next-generation intelligent information systems. Online polling is a kind of opinion mining; with the help of the NLP approach, it instantly grasps the user's sentiments and emotions.

Recommended Topics
  • Fine-grained sentiment analysis for next-generation intelligent information systems
  • Enhanced Sentiment learning algorithms for next-generation intelligent information systems
  • Lexicon-based methods-based sentiment analysis
  • Deep reinforcement learning for user-centric sentiment analysis
  • User-centric Hybrid sentiment analysis for next-generation intelligent information systems
  • Emotion detection and aspect-based sentiment analysis for next-generation intelligent information systems
  • Edge intelligence in user-centric opinion mining
  • AI-enabled user-centric opinion mining for information retrieval in next-generation intelligent information systems
  • Identifying, extracting and monitoring user-centric opinion in next-generation intelligent information systems
  • Natural language processing for user-centric sentiment analysis


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on User-Centric Opinion Mining and Sentiment Analysis for Next Generation Intelligent Information Systems on or before December 15th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Manoj Kumar
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-Mail: m.kumar@ddn.upes.ac.in

Special Issue On: Secure And Efficient Information Processing In Enterprise Systems With Artificial Intelligence (AI) And Federated Learning

Submission Due Date
12/25/2022

Guest Editors
Dr. BalaAnand Muthu, Adhiyamaan College of Engineering, India
Dr. Imran Shafique Ansari, University of Glasgow, United Kingdom
Dr. Xuan Liu, Yangzhou University, China

Introduction
Today's fast-changing world has compelled business enterprises to adhere to transformation to stay competitive. Also, Implementing advanced technologies in Enterprises come with a fair share of practical constraints. Since, many businesses cannot freely share data across multiple locations due to regulatory and performance issues when moving large amounts of data. Hence, An Enterprise system with information processing acts as a technological platform enabling organizations to coordinate and integrate business processes. It supports operations and business practices and works with supply chain and customer relationship management. Again, an enterprise Information system using enterprise software applies technologies that enable it to develop, operate, design and deploy applications at a large scale in Organizations.

Objective
Moreover, many enterprise Systems are powered by Machine Learning models for managing Enterprise's data. Still, advanced Information processing systems like Artificial Intelligence (AI) and Federated Learning are used to improve the security and privacy of critical data. It allows data networks to provide an effective solution without compromising user privacy. Federated learning is an Artificial Intelligence-based machine learning technique that holds data samples by applying a specific algorithm across edge devices or servers. It assists businesses and organizations in developing AI models to effectively and collaboratively maintain data confidentiality, data security during information processing.AI and Federated learning helps Machine Learning models train data across multiple platforms and cloud network. It provides a foundation for a machine learning framework to support a variety of learning topologies. It intends to provide a solid foundation with a wide range of topologies, particularly in a hybrid cloud environment. In addition, Federated learning Models can handle massive amounts of real-time data without worrying about users' privacy concerns. It can also solve ethical issues of different organizations like Healthcare, Finance, Industry, etc. In business organisations Federated learning and AI also enables hyper-personalization and highly contextual recommendations of vital Information. This special issue offers a forum for researchers and practitioners to develop Federated learning based on Artificial Intelligence for Enterprise systems to improve security of information transfer in Organizations.

Recommended Topics
  • Effective AI-based Federated Learning for secure Enterprise Information Systems
  • Federated Learning and AI algorithms in Organizations to improve data safety and security.
  • Advances in Information processing systems for enhancing data transfer in Enterprise.
  • The adaptive role of Federated Learning and AI for overcoming security threats in Enterprise Systems.
  • Applications of AI and Federated Learning for coordinating business processes in Enterprises.
  • State of the art AI based Federated Learning for proactively protecting enterprise data.
  • A new approach in Enterprise system with AI and Federated Learning to maintain data confidentiality.
  • Enhancing information security and privacy of data in Enterprise with AI and Federated Learning.
  • Federative Learning and Artificial Intelligence for Secure Information processing in Industries.
  • Novel AI and Federated learning models augmenting trust issues, privacy and security of Enterprise Systems.


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Secure And Efficient Information Processing In Enterprise Systems With Artificial Intelligence (AI) And Federated Learning on or before December 25, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

This is a full open access journal. Authors of manuscripts that are accepted to publish in this special issue will be expected to pay the article processing charge.



Open Access Resources:

All inquiries should be directed to the attention of:
Dr. BalaAnand Muthu
Dr. Imran Shafique Ansari
Dr. Xuan Liu
Guest Editor:
International Journal of Data Warehousing and Mining (IJDWM)
Email: balaanand@ieee.org; Imran.Ansari@glasgow.ac.uk; yusuf@yzu.edu.cn

Special Issue On: Data Mining for Cyber-Physical Systems and Complex, Time-Evolving Networks

Submission Due Date
12/29/2022

Guest Editors
Dr. Chi Lin, Dalian University of Technology, China (clindut@ieee.org)
Dr. Chang Wu Yu, Chung Hua University, Taiwan (cwyu@chu.edu.tw)
Dr. Ning Wang, Rowan University, United States (wangn@rowan.edu)

Introduction
Digital protection is a set of rules and innovations designed to protect our frameworks, organisation, and information from unapproved access, assaults, and undesirable interferes. In recent years, several market leaders in the IT industry have begun to use data mining techniques for malware detection. They intend to keep up with the secrecy, respectability, and accessibility of data and the executive's frameworks through different digital safeguard frameworks. Because of the accessibility of a large amount of information in digital framework and expanding number of digital hoodlums, there is a need for abilities to address network safety.

Objective

Internet technology has made the industry more hidden in various ways; in any case, this has disconnected separated us by implications that have traditionally never been sincerely adaptive, thus testing. As rapidly as stability was advanced, there was the cyberattack's image. A variety of data affirmation obligations are required about errands, information, and resources from ongoing arising assaults.

The use of security risk can prevent a lot of digitally enhanced attacks, privacy invasion, and documentation misunderstandings while still helping the panel's threat. Whenever the organisation grows an interpretation of coalition vigilance and a feasible contingency plan, it is easier to avoid and authenticate those very threats. A digital security detective promotes the security of the firm's paradigms and entities by planning and implementing our security measures. They provide troublesome responses to prevent relevant data from becoming taken, affected, or damaged. Moreover, tracking data exploitation investigates at least three linear models or repercussions. Communication data mining methods should be effective in combating these modifications. The use of evidence, mining algorithms have resulted in the establishment of a horrific quantity of knowledge that is far too large to purchase. Information is now becoming faster than our ability to process and sell actual data. The statistics must be limited as quickly as necessary to reach suitable data storage dimensions. Once knowledge or large amounts of data are recognised as the new daily investment of the era, the relevance of knowledge discovery and going to agree on techniques keeps increasing. Its fierce progression of effective methods us both to collect data hugely evaluated and sophisticated volumes of information. This special issue investigates the operability of data mining for cyber-physical systems and complex, time-evolving networks. Researchers are requested to describe emerging developments and advances in adopting time-evolving networks. Furthermore, there is a need to explore performance, metrics and challenges in data mining for cyber-physical systems and complex, time-evolving networks.



Recommended Topics
Empirical researchers on information mining frameworks. Multi-agent information extraction but also data deduplication oil and gas production Multiple data digging with outstanding quality and web-based data resource extraction Internet Of things implementations in the health care system would be examined under Cloud computing application domains. Statistical techniques are used in financial institutions through their financial services functionalities. Recent advances in deep learning and factual information mining techniques. The role of data mining in condition monitoring of high voltage electrical equipment. Strategies for the use of machine learning include reducing terrorist attacks. The role of Mining data for various biotic and abiotic negative issues. Arising patterns in network protection for The haze, suspicious messages, the sensor networks, and the dark web. Arising patterns in network protection for Machine intelligence, keyloggers, and procurement intrusions. Strategies for large amounts of data are excavated for exhibit, interpretation, and prospectus

Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Data Mining for Cyber-Physical Systems and Complex, Time-Evolving Networks on or before December 29th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations. http://www.igi-global.com/publish/contributor-resources/before-you-write/

All inquiries should be directed to the attention of:
Dr. Chi Lin
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-Mail: clindut@ieee.org

Special Issue On: Data Innovation & Data Science in Conservation Ecology

Submission Due Date
12/30/2022

Guest Editors
Dr. Muhammad Attique Khan, HITEC University, Pakistan (attique.khan@ieee.org)
Dr. Gaurav Dhiman, Government Bikram College of Commerce, India (gaurav.dhiman@thapar.edu)
Dr. Sathishkumar V. E., Hanyang University, Korea (sathishkumar@hanyang.ac.kr)

Introduction
In recent times, the ecosystem is changing tremendously. The concerns associated with the deterioration of biodiversity and the widespread epidemic of species extinction promotes a new stream of research on conservation ecology worldwide. In general, conservation ecology is the branch of ecology that deals with preserving and managing natural resources and biodiversity. It is a discipline that emerges rapidly to find ways in conserving species, ecosystem, landscape, and habitats in a quicker and efficient manner. The initiative needs to be smart, such that it makes a positive difference in the environment. The intersection of data science and ecology aids in developing cutting-edge technologies that help us protect the largest biodiversity crisis of our times. In this special issue, we will have a detailed look at how computational techniques can be applied to solve various ecological problems and biodiversity conservation. We aim to make an in-depth analysis of various ecological datasets (which contains data on vegetation, biodiversity, landscape, land use, land change, etc.) using data innovation and data science techniques.

Objective
There are many important aspects of conservation ecology, and with the advances in technology (remote sensing, GPS, and satellite imaging), a vast amount of ecological data is collected and produced every day. Typically, ecological problems are often considered to be complex, and they cannot be solved appropriately with data collection, modelling, and analysis using data science techniques. Data innovation is one of the ways to practice data science effectively, i.e. new data sources and methods are used to acquire a more nuanced understanding of the ecological data, which includes expressing solutions in a way that can be automated, effectively abstracting the problem into core mechanisms, and using the interaction between simulation and data to refine the actual problem and suggest new knowledge. The use of data science is common in most of the areas of conservation ecology, where it is mainly used to achieve the goal of solving “complex problems.” However, more innovative approaches are needed as the ecological data is highly complex, time-consuming, costly, possesses higher variability, and demanding to collect. Further, many of the data science models lack formal mathematical formulation to support ecological synthesis and help decision making.

Recommended Topics
  • Data innovation and data science in the context of conservation ecology
  • Innovative algorithms, software, and data management practices for conservation ecology
  • Solving complex problems in conservation ecology with the synergy of data science and big data analytics
  • Machine learning and big data analytics to enhance conservation efforts
  • Automated analysis of ecological data with data science tools and techniques
  • Biodiversity conservation with data science and big data
  • Going forward in conservation ecology with effective use of remote sensing data with data science technologies
  • Cost-effective data science models for conservation ecology
  • Predicting natural and present-day species extinction with data science techniques
  • Data science and conservation ecology: Trends and future research directions
  • Opportunities for data science in conservation and sustainability
  • Design and analysis of ecological data with artificial intelligence


Submission Procedure
Researchers and practitioners are invited to submit papers for this special theme issue on Data Innovation & Data Science in Conservation Ecology on or before December 30th, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

All inquiries should be directed to the attention of:
Dr. Muhammad Attique Khan
Guest Editor
International Journal of Data Warehousing and Mining (IJDWM)
E-Mail: attique.khan@ieee.org

Special Issue On: Big Data for E-Society: Realizing the Potential of Data Analytics for the Future Digital Era

Submission Due Date
12/30/2022

Guest Editors
Dr. C Chandru Vignesh, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India
Dr. Sivaparthipan C.B., Anna University, Coimbator, India
Dr. Adhiyaman Manickam, University of Moncton, Canada

Introduction
The evolution of innovative technologies has accelerated a socio-technical shift at an incredible speed and scale worldwide. Technology is undeniably playing a dramatic role in modernizing and enhancing the resiliency of human society in this emerging digital era. From financial markets to education, to healthcare, to governance, technology is critically transforming the way of our lives. Hence, our society is heavily reliant upon electronics and thus transforms into e-society, and its' technological trends such as the virtualization of education, communing, work, retail, entertainment, and many others are increasingly adopted. Among the prominent technologies like Artificial Intelligence, Internet of Things, cloud, and others, big data has been significantly holding a more significant potential in reshaping our society in future digital ea. As a consequence of digitalization, the sectors described above produce digital traces of data or information amounting to big data resources. Big data analytics, by analyzing massive data sets to extract all critical hidden patterns, unspecified interrelations, emerging market trends, people's preferences, and other complex tasks with limited or no human involvement. Therefore, big data and its' various applications are exploited in major industries such as e-health, business, natural resources, transportation, banking and securities, energy and utilities, and government for its tremendous advances. With the implementation of this software, these sectors can create various benefits like e-health records, imaging data, product quality, and defect tracking increasing energy efficiency, supporting mass-customization, predicting behaviour, scheduling optimization, and output forecasting. Further, it assists in personalized marketing, fuel optimization tools for the transportation industry, monitoring health conditions and predicting treatments through wearables, streamlined media streaming, real-time data monitoring, and cybersecurity protocols, decision-making, and a lot more.

Objective
The emerging trends of data analytics such as automation, democratization, user experience, and fragmented tools are revolutionizing businesses. Moreover, data analytics capabilities are also expected to become a driving force of various innovations in future smart cities. In addition, data analytics techniques can also change the agriculture methods like fertilizers, machinery, predict and enhance crop yields, and upgrade farming practices. Furthermore, the possibilities of data analytics solutions integrated with other advanced technologies can help develop incredible advances and opportunities in critical industries. These advancements of big data analytics can tremendously enhance the day-to-day lives of humankind in the advanced e-society. In this context, this special issue intends to explore the application of big data for e-society: Realizing the potential of data analytics for the future digital era. We invite researchers, practitioners, and scholars from various technology disciplines to present novel and innovative solutions for this special issue.

Recommended Topics
  • Role of disruptive technologies for the future of digital society.
  • Impact of big data analytics for decision-making in business.
  • Innovations of Big data analytics and society 5.0.
  • Digital transformation of society with AI and big data analytics.
  • Machine learning and big data for e-commerce.
  • Big data and cloud computing for the emerging digital era.
  • Opportunities and challenges of digital transformation of society.
  • Role of big data analytics on health, economy, and society.
  • Innovative big data applications for critical industries.
  • Data analytics for sustainable development of business.
  • Frontiers of big data analytics in supply chain & logistics.
  • Big data analytics & healthcare in smart cities.
  • AI and big data in the response of covid-19 pandemic.
  • Big data analytics for business & society.


Submission Procedure

Researchers and practitioners are invited to submit papers for this special theme issue on Big Data for E-Society: Realizing the Potential of Data Analytics for the Future Digital Era on or before December 30, 2022. All submissions must be original and may not be under review by another publication. INTERESTED AUTHORS SHOULD CONSULT THE JOURNAL’S GUIDELINES FOR MANUSCRIPT SUBMISSIONS at http://www.igi-global.com/publish/contributor-resources/before-you-write/. All submitted papers will be reviewed on a double-blind, peer review basis. Papers must follow APA style for reference citations.

This is a full open access journal. Authors of manuscripts that are accepted to publish in this special issue will be expected to pay the article processing charge.



Open Access Resources:

All inquiries should be directed to the attention of:
Dr. C Chandru Vignesh
Dr. Sivaparthipan C.B.
Dr. Adhiyaman Manickam
Guest Editor:
International Journal of Data Warehousing and Mining (IJDWM)
Email: drcchandruvignesh@veltech.edu.in; sivaparthipan@ieee.org; adhiyaman.m@ieee.org