Zhiwei Ye¹, Ziqian Fang¹, Zhina Song^1,*, Haigang Sui², Chunyan Yan¹, Wen Zhou¹, Mingwei Wang¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2019-2035, 2023, DOI:10.32604/iasc.2023.039949

Abstract Hyperparameters play a vital impact in the performance of most machine learning algorithms. It is a challenge for traditional methods to configure hyperparameters of the capsule network to obtain high-performance manually. Some swarm intelligence or evolutionary computation algorithms have been effectively employed to seek optimal hyperparameters as a combinatorial optimization problem. However, these algorithms are prone to get trapped in the local optimal solution as random search strategies are adopted. The inspiration for the hybrid rice optimization (HRO) algorithm is from the breeding technology of three-line hybrid rice in China, which has the advantages of easy implementation, less parameters and… More >

P. S. Arthy^1,*, A. Kavitha²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2959-2971, 2023, DOI:10.32604/iasc.2023.032511

Abstract With the advent of Machine and Deep Learning algorithms, medical image diagnosis has a new perception of diagnosis and clinical treatment. Regrettably, medical images are more susceptible to capturing noises despite the peak in intelligent imaging techniques. However, the presence of noise images degrades both the diagnosis and clinical treatment processes. The existing intelligent methods suffer from the deficiency in handling the diverse range of noise in the versatile medical images. This paper proposes a novel deep learning network which learns from the substantial extent of noise in medical data samples to alleviate this challenge. The proposed deep learning architecture… More >

K. Selvasheela^1,*, A. M. Abirami², Abdul Khader Askarunisa³

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2537-2552, 2023, DOI:10.32604/csse.2023.029148

Abstract Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review.… More >

Ala Saleh Alluhaidan¹, Masoud Alajmi², Fahd N. Al-Wesabi^3,4, Anwer Mustafa Hilal⁵, Manar Ahmed Hamza^5,*, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2713-2727, 2022, DOI:10.32604/cmc.2022.025202

Abstract Human fall detection (FD) acts as an important part in creating sensor based alarm system, enabling physical therapists to minimize the effect of fall events and save human lives. Generally, elderly people suffer from several diseases, and fall action is a common situation which can occur at any time. In this view, this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection (IAOA-DLFD) model to identify the fall/non-fall events. The proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image quality. Besides, the IAOA with Capsule Network based feature extractor is derived to… More >

Anurag Jain¹, Kusum Yadav², Hadeel Fahad Alharbi², Shamik Tiwari^1,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 423-438, 2022, DOI:10.32604/cmc.2022.020238

Abstract Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19… More >

Sohail Khan^1,*, Mohammad Nauman², Suleiman Ali Alsaif¹, Toqeer Ali Syed³, Hassan Ahmad Eleraky¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5345-5362, 2022, DOI:10.32604/cmc.2022.021271

Abstract Mobile phones are an essential part of modern life. The two popular mobile phone platforms, Android and iPhone Operating System (iOS), have an immense impact on the lives of millions of people. Among these two, Android currently boasts more than 84% market share. Thus, any personal data put on it are at great risk if not properly protected. On the other hand, more than a million pieces of malware have been reported on Android in just 2021 till date. Detecting and mitigating all this malware is extremely difficult for any set of human experts. Due to this reason, machine learning–and… More >

Sunqiang Hu¹, Xiaoyu Li¹, Yu Deng^1,*, Yu Peng¹, Bin Lin², Shan Yang³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 145-158, 2021, DOI:10.32604/cmc.2021.017441

Abstract In recent years, many text summarization models based on pre-training methods have achieved very good results. However, in these text summarization models, semantic deviations are easy to occur between the original input representation and the representation that passed multi-layer encoder, which may result in inconsistencies between the generated summary and the source text content. The Bidirectional Encoder Representations from Transformers (BERT) improves the performance of many tasks in Natural Language Processing (NLP). Although BERT has a strong capability to encode context, it lacks the fine-grained semantic representation. To solve these two problems, we proposed a semantic supervision method based on… More >

Shuqiao Liu, Junliang Li, Xiaojie Li^*

Journal on Internet of Things, Vol.2, No.4, pp. 135-144, 2020, DOI:10.32604/jiot.2020.09797

Abstract In the deep learning field, “Capsule” structure aims to overcome the shortcomings of traditional Convolutional Neural Networks (CNN) which are difficult to mine the relationship between sibling features. Capsule Net (CapsNet) is a new type of classification network structure with “Capsule” as network elements. It uses the “Squashing” algorithm as an activation function and Dynamic Routing as a network optimization method to achieve better classification performance. The main problem of the Brain Magnetic Resonance Imaging (Brain MRI) recognition algorithm is that the difference between Alzheimer’s disease (AD) image, the Mild Cognitive Impairment (MCI) image, and the normal image is not… More >

Jinyuan Li¹, Hao Li¹, Guorong Cui¹, Yan Kang^{1, *}, Yang Hu¹, Yingnan Zhou²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903

Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce adversarial training to improve performance… More >