Wei Chen^1,*, Xiaogang Wei^2,*, Hongxu Zhang², Wenpeng He²

Journal on Artificial Intelligence, Vol.6, pp. 225-240, 2024, DOI:10.32604/jai.2024.052366 - 16 August 2024

Abstract The Head-Related Transfer Function (HRTF) describes the effects of sound reflection and scattering caused by the environment and the human body when sound signals are transmitted from a source to the human ear. It contains a significant amount of auditory cue information used for sound localization. Consequently, HRTF renders 3D audio accurately in numerous immersive multimedia applications. Because HRTF is high-dimensional, complex, and nonlinear, it is a relatively large and intricate dataset, typically consisting of hundreds of thousands of samples. Storing HRTF requires a significant amount of storage space in practical applications. Based on this, More >

Sohail Muhammad¹, Muzammil Khan², Sarwar Shah Khan^2,3,*

Journal on Artificial Intelligence, Vol.6, pp. 193-209, 2024, DOI:10.32604/jai.2024.052099 - 07 August 2024

Abstract Retrieving information from evolving digital data collection using a user’s query is always essential and needs efficient retrieval mechanisms that help reduce the required time from such massive collections. Large-scale time consumption is certain to scan and analyze to retrieve the most relevant textual data item from all the documents required a sophisticated technique for a query against the document collection. It is always challenging to retrieve a more accurate and fast retrieval from a large collection. Text summarization is a dominant research field in information retrieval and text processing to locate the most appropriate… More >

Glody Muka^1,*, Patrick Mukala^1,2,*

Journal on Artificial Intelligence, Vol.6, pp. 211-223, 2024, DOI:10.32604/jai.2024.049685 - 07 August 2024

Abstract Reviews have a significant impact on online businesses. Nowadays, online consumers rely heavily on other people's reviews before purchasing a product, instead of looking at the product description. With the emergence of technology, malicious online actors are using techniques such as Natural Language Processing (NLP) and others to generate a large number of fake reviews to destroy their competitors’ markets. To remedy this situation, several researches have been conducted in the last few years. Most of them have applied NLP techniques to preprocess the text before building Machine Learning (ML) or Deep Learning (DL) models… More >

Rudraneel Dutta^*, Shreya Mohanty

Journal on Artificial Intelligence, Vol.6, pp. 177-192, 2024, DOI:10.32604/jai.2024.050706 - 19 July 2024

Abstract Traditionally, exam preparation involves manually analyzing past question papers to identify and prioritize key topics. This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation, Optical Character Recognition (OCR), and Latent Dirichlet Allocation (LDA) for topic modelling. This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers, aiding students in efficient exam preparation. The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify… More >

Kaveh Heidary^*

Journal on Artificial Intelligence, Vol.6, pp. 153-175, 2024, DOI:10.32604/jai.2024.050563 - 18 July 2024

Abstract This paper provides a detailed mathematical model governing the operation of feedforward neural networks (FFNN) and derives the backpropagation formulation utilized in the training process. Network protection systems must ensure secure access to the Internet, reliability of network services, consistency of applications, safeguarding of stored information, and data integrity while in transit across networks. The paper reports on the application of neural networks (NN) and deep learning (DL) analytics to the detection of network traffic anomalies, including network intrusions, and the timely prevention and mitigation of cyberattacks. Among the most prevalent cyber threats are R2L,… More >

Adil Hussain^1,*, Ayesha Aslam²

Journal on Artificial Intelligence, Vol.6, pp. 129-152, 2024, DOI:10.32604/jai.2024.050277 - 21 May 2024

Abstract The diagnosis and prognosis of cardiovascular diseases are critical medical responsibilities that assist cardiologists in correctly classifying patients and treating them accordingly. The utilization of machine learning in the medical domain has witnessed a notable surge due to its ability to discern patterns from vast amounts of data. Machine learning algorithms that can categorize cases of cardiovascular illness may help doctors reduce the number of wrong diagnoses. This research investigates the efficacy of different machine learning algorithms in predicting cardiovascular disease in accordance with risk factors. This study utilizes a variety of machine learning models, More >

Hina Batool¹, Jiuyun Xu^1,*, Ateeq Ur Rehman², Habib Hamam^3,4,5,6

Journal on Artificial Intelligence, Vol.6, pp. 105-128, 2024, DOI:10.32604/jai.2024.049912 - 06 May 2024

Abstract Federated Learning (FL) appeared as an encouraging approach for handling decentralized data. Creating a FL system needs both machine learning (ML) knowledge and thinking about how to design system software. Researchers have focused a lot on the ML side of FL, but have not paid enough attention to designing the software architecture. So, in this survey, a set of design patterns is described to tackle the design issues. Design patterns are like reusable solutions for common problems that come up when designing software architecture. This paper focuses on (1) design patterns such as architectures, frameworks,… More >

Farhad Mortezapour Shiri^1,*, Ehsan Ahmadi², Mohammadreza Rezaee¹, Thinagaran Perumal¹

Journal on Artificial Intelligence, Vol.6, pp. 85-103, 2024, DOI:10.32604/jai.2024.048911 - 24 April 2024

Abstract Automatic detection of student engagement levels from videos, which is a spatio-temporal classification problem is crucial for enhancing the quality of online education. This paper addresses this challenge by proposing four novel hybrid end-to-end deep learning models designed for the automatic detection of student engagement levels in e-learning videos. The evaluation of these models utilizes the DAiSEE dataset, a public repository capturing student affective states in e-learning scenarios. The initial model integrates EfficientNetV2-L with Gated Recurrent Unit (GRU) and attains an accuracy of 61.45%. Subsequently, the second model combines EfficientNetV2-L with bidirectional GRU (Bi-GRU), yielding More >

Umar Isa Abdulkadir^*, Anil Fernando^*

Journal on Artificial Intelligence, Vol.6, pp. 71-83, 2024, DOI:10.32604/jai.2024.045332 - 22 April 2024

Abstract One of the significant issues the insurance industry faces is its ability to predict future claims related to individual policyholders. As risk varies from one policyholder to another, the industry has faced the challenge of using various risk factors to accurately predict the likelihood of claims by policyholders using historical data. Traditional machine-learning models that use neural networks are recognized as exceptional algorithms with predictive capabilities. This study aims to develop a deep learning model using sequential deep regression techniques for insurance claim prediction using historical data obtained from Kaggle with 1339 cases and eight… More >

Yuting Xu^1,*, Deqing Zhang¹, Huaibei Guo², Mengyue Wang¹

Journal on Artificial Intelligence, Vol.6, pp. 53-69, 2024, DOI:10.32604/jai.2024.049083 - 05 April 2024

Abstract In multi-label learning, the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data. The classification performance and robustness of the model are effectively improved. Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation. It is well known that the correlation between labels is asymmetric. However, existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels. Based on this, this paper proposes a Causality-driven Common and Label-specific Features Learning, named CCSF More >