Yunlei Zhang¹, Ruifeng Cao¹, Danhuang Dong², Sha Peng^3,*, Ruoyun Du³, Xiaomin Xu³

Energy Engineering, Vol.119, No.5, pp. 1829-1841, 2022, DOI:10.32604/ee.2022.020118

Abstract In the electricity market, fluctuations in real-time prices are unstable, and changes in short-term load are determined by many factors. By studying the timing of charging and discharging, as well as the economic benefits of energy storage in the process of participating in the power market, this paper takes energy storage scheduling as merely one factor affecting short-term power load, which affects short-term load time series along with time-of-use price, holidays, and temperature. A deep learning network is used to predict the short-term load, a convolutional neural network (CNN) is used to extract the features, and a long short-term memory… More >

Liyuan Xin^1,2,3, Xiang Rao^1,2,3,*, Xiaoyin Peng^1,2,3, Yunfeng Xu^1,2,3, Jiating Chen^1,2,3

Energy Engineering, Vol.119, No.5, pp. 1905-1922, 2022, DOI:10.32604/ee.2022.019556

Abstract The rapid production dynamic prediction of water-flooding reservoirs based on well location deployment has been the basis of production optimization of water-flooding reservoirs. Considering that the construction of geological models with traditional numerical simulation software is complicated, the computational efficiency of the simulation calculation is often low, and the numerical simulation tools need to be repeated iteratively in the process of model optimization, machine learning methods have been used for fast reservoir simulation. However, traditional artificial neural network (ANN) has large degrees of freedom, slow convergence speed, and complex network model. This paper aims to predict the production performance of… More >

V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2413-2429, 2023, DOI:10.32604/iasc.2023.030527

Abstract In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits and create threat hunts on… More >

R. Saravana Ram¹, M. Vinoth Kumar², Tareq M. Al-shami³, Mehedi Masud⁴, Hanan Aljuaid⁵, Mohamed Abouhawwash^6,7,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2449-2462, 2023, DOI:10.32604/iasc.2023.030486

Abstract Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision features of the image frames… More >

S. Neelavathy Pari^1,*, K. Sudharson²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2541-2554, 2023, DOI:10.32604/iasc.2023.030284

Abstract The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority. The intensity of the harm largely depends on the attacker’s intentions during hostile assaults. As a result, the loss of Information, power, or capacity may occur. The authors propose an Enhanced Trust-Based Secure Route Protocol (ETBSRP) using features extraction. First, the primary and secondary trust characteristics are retrieved and achieved routing using a calculation. The complete trust characteristic obtains by integrating all logical and physical trust from every node. To assure intermediate node trustworthiness, we designed an ETBSRP, and it calculates and… More >

S. M. Udhaya Sankar^1,*, Mary Subaja Christo², P. S. Uma Priyadarsini³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2337-2351, 2023, DOI:10.32604/iasc.2023.030278

Abstract Energy conservation has become a significant consideration in wireless sensor networks (WSN). In the sensor network, the sensor nodes have internal batteries, and as a result, they expire after a certain period. As a result, expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efficient way remains a challenge. Also, the clustering strategy employs to enhance or extend the life cycle of WSNs. We identify the supervisory head node (SH) or cluster head (CH) in every grouping considered the feasible strategy for power-saving route discovery in the clustering model, which diminishes the communication… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168

Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The model performance is verified with… More >

Lina M. K. Al-Ebbini^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1971-1985, 2023, DOI:10.32604/iasc.2023.030100

Abstract A relationship between lung transplant success and many features of recipients’/donors has long been studied. However, modeling a robust model of a potential impact on organ transplant success has proved challenging. In this study, a hybrid feature selection model was developed based on ant colony optimization (ACO) and k-nearest neighbor (kNN) classifier to investigate the relationship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing (UNOS). The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’ functional status (i.e., quality of life) after… More >

S. Sathish Kumar^1,*, G. Ravi²

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2367-2382, 2023, DOI:10.32604/iasc.2023.030054

Abstract Due to the scattered nature of the network, data transmission in a distributed Mobile Ad-hoc Network (MANET) consumes more energy resources (ER) than in a centralized network, resulting in a shorter network lifespan (NL). As a result, we build an Enhanced Opportunistic Routing (EORP) protocol architecture in order to address the issues raised before. This proposed routing protocol goal is to manage the routing cost by employing power, load, and delay to manage the routing energy consumption based on the flooding of control packets from the target node. According to the goal of the proposed protocol technique, it is possible… More >

B. Chellapraba^1,*, D. Manohari², K. Periyakaruppan³, M. S. Kavitha⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2353-2366, 2023, DOI:10.32604/iasc.2023.029923

Abstract Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI) classification (RDADL-HIC) technique. The proposed… More >