Special Issue "Innovative Technology For Machine Intelligence"

Submission Deadline: 27 September 2021 (closed)
Guest Editors
Dr. Kong Fah TEE, University of Greenwich, United Kingdom.
Dr. Abdul Quadir Md, Vellore Institute of Technology (VIT), India.
Dr. Arun Kumar Sivaraman, Vellore Institute of Technology (VIT), India.


There is an extraordinary breakthrough of research accomplishments in using machine learning and deep learning, which demonstrate massive prospective in making resolutions, improving operations, alleviating security dangers and also in a diversity of domains, such as healthcare, insurance, marketing, commercialization and industry. The substantial progressions in artificial technologies motivated the affluences of the domains in machine learning and deep learning algorithms, correspondingly. Fresh advances in the machine learning and deep learning field have showed exceptional performance in managing data with respect to time and space in precise fields like image, audio, and video. In the meantime, the expansion of sensing and information collection methods in applicable domains have permitted and accrued outsized scale of spatiotemporal information over the period of time, which has headed to extraordinary prerequisites and openings for the discovery of large- and diminutive- spatiotemporal singularities which are specific and accurate.

In this call, we search for extraordinary quality research articles that can validate feasibility study, services, results for investigation issues, thematic studies, analytics, current world scenarios and effective deliveries of Machine Learning and Deep Learning applications.


- Deep learning for healthcare data processing and medical processing

- Deep learning for smart business and economic

- Deep learning for present learning and education

- Mobile network with deep learning

- Deep learning for energy solicitations and services

- Deep learning for physical science, meteorological prediction and geoscience

- Artificial Intelligence for smart cities

- Learning and adaptive systems using Machine Learning

- Machine Learning and mobile network for security, privacy and trust

- Machine Learning and Cybersecurity

- Machine Learning and Computer vision with wireless network

- Machine Learning and Computer Vision for any forms of analytical demonstration and analytics

- Machine Learning for Bio Medical system

Machine Learning, Computational Intelligence, Intelligent Data Processing, Computer Vision, Neural Network, Medical Imaging, Internet of Things, Artificial Intelligence, Signal Processing, Data Fusion Techniques

Published Papers
  • Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction
  • Abstract Data is always a crucial issue of concern especially during its prediction and computation in digital revolution. This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication. It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data. The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means. The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters… More
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  • An Adaptive Classifier Based Approach for Crowd Anomaly Detection
  • Abstract Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and security. Intelligent video surveillance systems make extensive use of data mining, machine learning and deep learning methods. In this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep learning. In this approach, Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded scenes. We use multiple instance learning (MIL) to dynamically develop a deep anomalous ranking framework. This technique predicts higher anomalous values for abnormal video frames by treating regular… More
  •   Views:411       Downloads:358        Download PDF

  • Transfer Learning-based Computer-aided Diagnosis System for Predicting Grades of Diabetic Retinopathy
  • Abstract Diabetic retinopathy (DR) diagnosis through digital fundus images requires clinical experts to recognize the presence and importance of many intricate features. This task is very difficult for ophthalmologists and time-consuming. Therefore, many computer-aided diagnosis (CAD) systems were developed to automate this screening process of DR. In this paper, a CAD-DR system is proposed based on preprocessing and a pre-train transfer learning-based convolutional neural network (PCNN) to recognize the five stages of DR through retinal fundus images. To develop this CAD-DR system, a preprocessing step is performed in a perceptual-oriented color space to enhance the DR-related lesions and then a standard… More
  •   Views:535       Downloads:463        Download PDF