Sundas Rukhsar¹, Mazhar Javed Awan¹, Usman Naseem², Dilovan Asaad Zebari³, Mazin Abed Mohammed^4,*, Marwan Ali Albahar⁵, Mohammed Thanoon⁵, Amena Mahmoud⁶

Computer Systems Science and Engineering, Vol.47, No.1, pp. 791-807, 2023, DOI:10.32604/csse.2023.038384

Abstract Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions, sentiments, and data in the modern era. Twitter, a widely used microblogging site where individuals share their thoughts in the form of tweets, has become a major source for sentiment analysis. In recent years, there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets. Opinions or expressions of people about a particular topic, situation, person, or product can be identified from sentences and divided into three categories: positive for good, negative for bad, and neutral for… More >

Z. Faizal Khan¹, Saeed M. Alshahrani^2,*, Abdulrahman Alghamdi², Someah Alangari³, Nouf Ibrahim Altamami⁴, Khalid A. Alissa⁵, Sana Alazwari⁶, Mesfer Al Duhayyim⁷, Fahd N. Al-Wesabi⁸

Computer Systems Science and Engineering, Vol.47, No.1, pp. 855-871, 2023, DOI:10.32604/csse.2023.036735

Abstract The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly… More >

Sindhuja Manickavasagam^*, Poonkuzhali Sugumaran

Computer Systems Science and Engineering, Vol.47, No.1, pp. 135-151, 2023, DOI:10.32604/csse.2023.030344

Abstract Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes… More >

Nisha Vasudevan^1,*, Vasudevan Venkatraman², A. Ramkumar¹, T. Muthukumar³, A. Sheela⁴, M. Vetrivel⁵, R. J. Vijaya Saraswathi⁶, F. T. Josh⁷

Energy Engineering, Vol.120, No.8, pp. 1747-1761, 2023, DOI:10.32604/ee.2023.027821

Abstract MigroGrid (MG) has emerged to resolve the growing demand for energy. But because of its inconsistent output, it can result in various power quality (PQ) issues. PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources. Similarly, the employment of nonlinear loads will introduce harmonics into the system and, as a result, cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system. Thus, this research focuses on power quality enhancement in the MG using hybrid shunt filters. However, the… More >

Christopher C. Rosser, Wilbur L. Walters, Abdulghani M. Abdulghani, Mokhles M. Abdulghani, Khalid H. Abed^*

Journal on Artificial Intelligence, Vol.4, No.4, pp. 261-277, 2022, DOI:10.32604/jai.2022.039703

Abstract Intrinsic motivation helps autonomous exploring agents traverse a larger portion of their environments. However, simulations of different learning environments in previous research show that after millions of timesteps of successful training, an intrinsically motivated agent may learn to act in ways unintended by the designer. This potential for unintended actions of autonomous exploring agents poses threats to the environment and humans if operated in the real world. We investigated this topic by using Unity’s Machine Learning Agent Toolkit (ML-Agents) implementation of the Proximal Policy Optimization (PPO) algorithm with the Intrinsic Curiosity Module (ICM) to train autonomous exploring agents in three… More >

Salah Alzghoul^1,*, Othman Smadi¹, Ali Al Bataineh², Mamon Hatmal³, Ahmad Alamm⁴

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 455-470, 2023, DOI:10.32604/iasc.2023.038338

Abstract Currently, the decision of aortic valve replacement surgery time for asymptomatic patients with moderate-to-severe aortic stenosis (AS) is made by healthcare professionals based on the patient’s clinical biometric records. A delay in surgical aortic valve replacement (SAVR) can potentially affect patients’ quality of life. By using ML algorithms, this study aims to predict the optimal SAVR timing and determine the enhancement in moderate-to-severe AS patient survival following surgery. This study represents a novel approach that has the potential to improve decision-making and, ultimately, improve patient outcomes. We analyze data from 176 patients with moderate-to-severe aortic stenosis who had undergone or… More >

Yue Yu¹, Junhua Wu^1,*, Guangshun Li¹, Wangang Wang²

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 583-598, 2023, DOI:10.32604/iasc.2023.037875

Abstract As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects.… 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 >

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 >

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 >