Jianbo Lu^1,2,*, Ruxin Shi², Jin Tong³, Wenqi Cheng⁴, Xiaoya Ma^1,3, Xiaobin Liu²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 525-544, 2023, DOI:10.32604/iasc.2023.038287

Abstract In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into the ShuffleNetV2 model network structure… More >

S. Balaji^*, S. Karthik

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409

Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for… More >

Jiajia Yu^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 777-797, 2023, DOI:10.32604/iasc.2023.037354

Abstract This study aims to reduce the interference of ambient noise in mobile communication, improve the accuracy and authenticity of information transmitted by sound, and guarantee the accuracy of voice information delivered by mobile communication. First, the principles and techniques of speech enhancement are analyzed, and a fast lateral recursive least square method (FLRLS method) is adopted to process sound data. Then, the convolutional neural networks (CNNs)-based noise recognition CNN (NR-CNN) algorithm and speech enhancement model are proposed. Finally, related experiments are designed to verify the performance of the proposed algorithm and model. The experimental results show that the noise classification… More >

Alia Alshehri, Duaa AlSaeed^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different models, which is overlooked in… More >

Asma Qaiser¹, Saman Hina¹, Abdul Karim Kazi^1,*, Saad Ahmed², Raheela Asif³

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 73-90, 2023, DOI:10.32604/iasc.2023.036784

Abstract In the past few years, social media and online news platforms have played an essential role in distributing news content rapidly. Consequently. verification of the authenticity of news has become a major challenge. During the COVID-19 outbreak, misinformation and fake news were major sources of confusion and insecurity among the general public. In the first quarter of the year 2020, around 800 people died due to fake news relevant to COVID-19. The major goal of this research was to discover the best learning model for achieving high accuracy and performance. A novel case study of the Fake News Classification using… More >

Arfa Siddiqu, Abdul Basit^*, Waheed Noor, Muhammad Asfandyar Khan, M. Saeed H. Kakar, Azam Khan

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 875-885, 2023, DOI:10.32604/iasc.2023.036426

Abstract The optical character recognition for the right to left and cursive languages such as Arabic is challenging and received little attention from researchers in the past compared to the other Latin languages. Moreover, the absence of a standard publicly available dataset for several low-resource languages, including the Pashto language remained a hurdle in the advancement of language processing. Realizing that, a clean dataset is the fundamental and core requirement of character recognition, this research begins with dataset generation and aims at a system capable of complete language understanding. Keeping in view the complete and full autonomous recognition of the cursive… More >

Cherifa Nakkach^*, Amira Zrelli, Tahar Ezzedine

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 545-560, 2023, DOI:10.32604/iasc.2023.036385

Abstract Due to the development of diversified and flexible building energy resources, the balancing energy supply and demand especially in smart buildings caused an increasing problem. Energy forecasting is necessary to address building energy issues and comfort challenges that drive urbanization and consequent increases in energy consumption. Recently, their management has a great significance as resources become scarcer and their emissions increase. In this article, we propose an intelligent energy forecasting method based on hybrid deep learning, in which the data collected by the smart home through meters is put into the pre-evaluation step. Next, the refined data is the input… More >

K. Chanthirasekaran, Raghu Gundaala^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 471-489, 2023, DOI:10.32604/iasc.2023.036130

Abstract When it comes to decreasing margins and increasing energy efficiency in near-threshold and sub-threshold processors, timing error resilience may be viewed as a potentially lucrative alternative to examine. On the other hand, the currently employed approaches have certain restrictions, including high levels of design complexity, severe time constraints on error consolidation and propagation, and uncontaminated architectural registers (ARs). The design of near-threshold circuits, often known as NT circuits, is becoming the approach of choice for the construction of energy-efficient digital circuits. As a result of the exponentially decreased driving current, there was a reduction in performance, which was one of… More >

Hala J. Alshahrani¹, Abdulbaset Gaddah², Ehab S. Alnuzaili³, Mesfer Al Duhayyim^4,*, Heba Mohsen⁵, Ishfaq Yaseen⁶, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 283-300, 2023, DOI:10.32604/iasc.2023.035334

Abstract Sentiment analysis (SA) is a growing field at the intersection of computer science and computational linguistics that endeavors to automatically identify the sentiment presented in text. Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language. Sentiment is classified as a negative or positive assessment articulated through language. SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online (of a movie) can be negative or positive toward the thing that has been reviewed. Deep learning (DL) is becoming a powerful machine… More >