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Computational Collective Intelligence with Big Data–AI Society
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Computational Collective Intelligence with Big Data–AI Society

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 18990

Special Issue Editors

Dr. Sathishkumar Veerappampalayam Easwaramoorthy

E-Mail Website
Guest Editor
Department of Industrial Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul 04763, Republic of Korea
Interests: data mining; machine learning; big data analytics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Malliga Subramanian

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Tamilnadu, India
Interests: network security; internet of things; medical image analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Collective Intelligence refers to a type of intelligence typically believed to be an intelligence that emerges because of a group of autonomous units such as people, systems, etc., focused on accomplishing a task. Computational collective intelligence can be defined as the form of intelligence that results from the cooperation and competition of many individuals. Computational Collective Intelligence focuses developing methodologies and algorithms for addressing issues that arise while processing collective knowledge. These techniques are used to process information and knowledge that has come from decentralized and independent sources. The objective of Computational Collective Intelligence is to explore new methodological, theoretical, and practical aspects of computational collective intelligence. People may solve many problems by sharing their experiences with their colleagues and by passing on their expertise through online networks. In addition, recent developments in web technologies, pervasive and ubiquitous systems and networks, cloud and highly distributed computing systems, and the availability of massive amounts of data have altered the field of computer-supported collaboration, specifically with the emergence of new capabilities and forms of collective intelligence. The transformation of such huge amounts of information into big knowledge and the creation of a knowledge-based system require new perspectives and methods to present individuals with improved opportunities for collaboration.

However, when the data sets are bigger, more computing power is needed. For structured data, you need computers with more power. On the other hand, it will be important to use collective intelligence for unstructured data. With the rise of big data, much research is needed to understand how to combine "big data" with "collective intelligence." Collective intelligence has been a significant research topic in many AI communities. Therefore, the goal of the Special Issue is to use computational collective intelligence to find solutions to the problems of processing big data. The potential topics include but are not limited to the following:

  1. Big data and knowledge representation;
  2. Knowledge discovery from big data;
  3. Collective intelligence from social data;
  4. Applications of computational collective intelligence;
  5. collective computational intelligence for medial image and any other domain;
  6. Natural language processing and computational collective intelligence.

This Special Issue aims to bring researchers working in the field of computational collective intelligence together to exchange ideas, learn from each other's work, and solve problems related to big data processing. It is believed that this topic will provide an opportunity to advance the conversation about the potential of collective intelligence across a wide range of communities.

Dr. Sathishkumar Veerappampalayam Easwaramoorthy
Dr. Malliga Subramanian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational collective intelligence
  • data mining
  • knowledge representation and reasoning
  • planning and scheduling
  • adaptive multi-agent systems
  • healthcare
  • genetic programming
  • knowledge management
  • distributed artificial intelligence
  • artificial neural networks
  • biometrics
  • big data
  • cryptography
  • computer vision

Published Papers (5 papers)

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Research

20 pages, 6486 KiB  
Article
Recognizing Similar Musical Instruments with YOLO Models
by Christine Dewi, Abbott Po Shun Chen and Henoch Juli Christanto
Big Data Cogn. Comput. 2023, 7(2), 94; https://doi.org/10.3390/bdcc7020094 - 10 May 2023
Cited by 6 | Viewed by 2659
Abstract
Researchers in the fields of machine learning and artificial intelligence have recently begun to focus their attention on object recognition. One of the biggest obstacles in image recognition through computer vision is the detection and identification of similar items. Identifying similar musical instruments [...] Read more.
Researchers in the fields of machine learning and artificial intelligence have recently begun to focus their attention on object recognition. One of the biggest obstacles in image recognition through computer vision is the detection and identification of similar items. Identifying similar musical instruments can be approached as a classification problem, where the goal is to train a machine learning model to classify instruments based on their features and shape. Cellos, clarinets, erhus, guitars, saxophones, trumpets, French horns, harps, recorders, bassoons, and violins were all classified in this investigation. There are many different musical instruments that have the same size, shape, and sound. In addition, we were amazed by the simplicity with which humans can identify items that are very similar to one another, but this is a challenging task for computers. For this study, we used YOLOv7 to identify pairs of musical instruments that are most like one another. Next, we compared and evaluated the results from YOLOv7 with those from YOLOv5. Furthermore, the results of our tests allowed us to enhance the performance in terms of detecting similar musical instruments. Moreover, with an average accuracy of 86.7%, YOLOv7 outperformed previous approaches and other research results. Full article
(This article belongs to the Special Issue Computational Collective Intelligence with Big Data–AI Society)
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18 pages, 2477 KiB  
Article
A Gradient Boosted Decision Tree-Based Influencer Prediction in Social Network Analysis
by Neelakandan Subramani, Sathishkumar Veerappampalayam Easwaramoorthy, Prakash Mohan, Malliga Subramanian and Velmurugan Sambath
Big Data Cogn. Comput. 2023, 7(1), 6; https://doi.org/10.3390/bdcc7010006 - 05 Jan 2023
Cited by 5 | Viewed by 3089
Abstract
Twitter, Instagram and Facebook are expanding rapidly, reporting on daily news, social activities and regional or international actual occurrences. Twitter and other platforms have gained popularity because they allow users to submit information, links, photos and videos with few restrictions on content. As [...] Read more.
Twitter, Instagram and Facebook are expanding rapidly, reporting on daily news, social activities and regional or international actual occurrences. Twitter and other platforms have gained popularity because they allow users to submit information, links, photos and videos with few restrictions on content. As a result of technology advances (“big” data) and an increasing trend toward institutionalizing ethics regulation, social network analysis (SNA) research is currently confronted with serious ethical challenges. A significant percentage of human interactions occur on social networks online. In this instance, content freshness is essential, as content popularity declines with time. Therefore, we investigate how influencer content (i.e., posts) generates interactions, as measured by the number of likes and reactions. The Gradient Boosted Decision Tree (GBDT) and the Chaotic Gradient-Based Optimizer are required for estimation (CGBO). Using earlier group interactions, we develop the Influencers Prediction issue in this study’s setting of SN-created groups. We also provide a GBDT-CGBO framework and an efficient method for identifying users with the ability to influence the future behaviour of others. Our contribution is based on logic, experimentation and analytic techniques. The goal of this paper is to find domain-based social influencers using a framework that uses semantic analysis and machine learning modules to measure and predict users’ credibility in different domains and at different times. To solve these problems, future research will have to focus on co-authorship networks and economic networks instead of online social networks. The results show that our GBDT-CGBO method is both useful and effective. Based on the test results, the GBDT-CGBO model can correctly classify unclear data, which speeds up processing and makes it more efficient. Full article
(This article belongs to the Special Issue Computational Collective Intelligence with Big Data–AI Society)
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16 pages, 4442 KiB  
Article
Yolov5 Series Algorithm for Road Marking Sign Identification
by Christine Dewi, Rung-Ching Chen, Yong-Cun Zhuang and Henoch Juli Christanto
Big Data Cogn. Comput. 2022, 6(4), 149; https://doi.org/10.3390/bdcc6040149 - 07 Dec 2022
Cited by 10 | Viewed by 4764
Abstract
Road markings and signs provide vehicles and pedestrians with essential information that assists them to follow the traffic regulations. Road surface markings include pedestrian crossings, directional arrows, zebra crossings, speed limit signs, other similar signs and text, and so on, which are usually [...] Read more.
Road markings and signs provide vehicles and pedestrians with essential information that assists them to follow the traffic regulations. Road surface markings include pedestrian crossings, directional arrows, zebra crossings, speed limit signs, other similar signs and text, and so on, which are usually painted directly onto the road surface. Road markings fulfill a variety of important functions, such as alerting drivers to the potentially hazardous road section, directing traffic, prohibiting certain actions, and slowing down. This research paper provides a summary of the Yolov5 algorithm series for road marking sign identification, which includes Yolov5s, Yolov5m, Yolov5n, Yolov5l, and Yolov5x. This study explores a wide range of contemporary object detectors, such as the ones that are used to determine the location of road marking signs. Performance metrics monitor important data, including the quantity of BFLOPS, the mean average precision (mAP), and the detection time (IoU). Our findings shows that Yolov5m is the most stable method compared to other methods with 76% precision, 86% recall, and 83% mAP during the training stage. Moreover, Yolov5m and Yolov5l achieve the highest score, mAP 87% on average in the testing stage. In addition, we have created a new dataset for road marking signs in Taiwan, called TRMSD. Full article
(This article belongs to the Special Issue Computational Collective Intelligence with Big Data–AI Society)
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13 pages, 879 KiB  
Article
Trust-Based Data Communication in Wireless Body Area Network for Healthcare Applications
by Sangeetha Ramaswamy and Usha Devi Gandhi
Big Data Cogn. Comput. 2022, 6(4), 148; https://doi.org/10.3390/bdcc6040148 - 01 Dec 2022
Cited by 3 | Viewed by 1929
Abstract
A subset of Wireless Sensor Networks, Wireless Body Area Networks (WBAN) is an emerging technology. WBAN is a collection of tiny pieces of wireless body sensors with small computational capability, communicating short distances using ZigBee or Bluetooth, an application mainly in the healthcare [...] Read more.
A subset of Wireless Sensor Networks, Wireless Body Area Networks (WBAN) is an emerging technology. WBAN is a collection of tiny pieces of wireless body sensors with small computational capability, communicating short distances using ZigBee or Bluetooth, an application mainly in the healthcare industry like remote patient monitoring. The small piece of sensor monitors health factors like body temperature, pulse rate, ECG, heart rate, etc., and communicates to the base station or central coordinator for aggregation or data computation. The final data is communicated to remote monitoring devices through the internet or cloud service providers. The main challenge for this technology is energy consumption and secure communication within the network and the possibility of attacks executed by malicious nodes, creating problems for the network. This system proposes a suitable trust model for secure communication in WBAN based on node trust and data trust. Node trust is calculated using direct trust calculation and node behaviours. The data trust is calculated using consistent data success and data aging. The performance is compared with an existing protocol like Trust Evaluation (TE)-WBAN and Body Area Network (BAN)-Trust which is not a cryptographic technique. The protocol is lightweight and has low overhead. The performance is rated best for Throughput, Packet Delivery Ratio, and Minimum delay. With extensive simulation on-off attacks, Selfishness attacks, sleeper attacks, and Message suppression attacks were prevented. Full article
(This article belongs to the Special Issue Computational Collective Intelligence with Big Data–AI Society)
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14 pages, 307 KiB  
Article
A Survey on Medical Image Segmentation Based on Deep Learning Techniques
by Jayashree Moorthy and Usha Devi Gandhi
Big Data Cogn. Comput. 2022, 6(4), 117; https://doi.org/10.3390/bdcc6040117 - 17 Oct 2022
Cited by 18 | Viewed by 4882
Abstract
Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep [...] Read more.
Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep learning concepts applicable to medical image segmentation. The study of deep learning can be applied to image categorization, object recognition, segmentation, registration, and other tasks. First, the basic ideas of deep learning techniques, applications, and frameworks are introduced. Deep learning techniques that operate the ideal applications are briefly explained. This paper indicates that there is a previous experience with different techniques in the class of medical image segmentation. Deep learning has been designed to describe and respond to various challenges in the field of medical image analysis such as low accuracy of image classification, low segmentation resolution, and poor image enhancement. Aiming to solve these present issues and improve the evolution of medical image segmentation challenges, we provide suggestions for future research. Full article
(This article belongs to the Special Issue Computational Collective Intelligence with Big Data–AI Society)
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