Aigerim Baimakhanova^1,*, Ainur Zhumadillayeva², Bigul Mukhametzhanova³, Natalya Glazyrina², Rozamgul Niyazova², Nurseit Zhunissov¹, Aizhan Sambetbayeva⁴

Computer Systems Science and Engineering, Vol.48, No.3, pp. 793-811, 2024, DOI:10.32604/csse.2024.043273

Abstract As digital technologies have advanced more rapidly, the number of paper documents recently converted into a digital format has exponentially increased. To respond to the urgent need to categorize the growing number of digitized documents, the classification of digitized documents in real time has been identified as the primary goal of our study. A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human involvement. Artificial intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits, which were not… More >

Md. Munirul Hasan¹, Md Mustafizur Rahman^2,*, Mohammad Saiful Islam³, Wong Hung Chan⁴, Yasser M. Alginahi⁵, Muhammad Nomani Kabir⁶, Suraya Abu Bakar¹, Devarajan Ramasamy²

Frontiers in Heat and Mass Transfer, Vol.22, No.2, pp. 537-556, 2024, DOI:10.32604/fhmt.2024.047428

Abstract A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature range. In most radiators that are used to cool an engine, water serves as a cooling fluid. The performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling water. Concentration and uniformity of nanoparticle distribution are the two major factors for the practical use of nanofluids. The shape and size of nanoparticles also have a great impact on the performance of heat transfer. Many researchers are… More > Graphic Abstract

Chao-Lung Yang^1,*, Atinkut Atinafu Yilma^1,2, Bereket Haile Woldegiorgis², Hendrik Tampubolon^3,4, Hendri Sutrisno⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 233-254, 2024, DOI:10.32604/iasc.2024.043091

Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random… More >

Muhamad Irsan^1,2,*, Rosilah Hassan², Anwar Hassan Ibrahim³, Mohamad Khatim Hasan², Meng Chun Lam², Wan Mohd Hirwani Wan Hussain⁴

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 157-174, 2024, DOI:10.32604/iasc.2024.042193

Abstract One of the major causes of road accidents is sleepy drivers. Such accidents typically result in fatalities and financial losses and disadvantage other road users. Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system. Most studies have examined how the mouth and eyelids move. However, this limits the system’s ability to identify drowsiness traits. Therefore, this study designed an Accident Detection Framework (RPK) that could be used to reduce road accidents due to sleepiness and detect the location of accidents. The drowsiness detection model used three facial… More >

Abeer Abdullah Alsadhan¹, Abdullah A. Al-Atawi², Hanen karamti³, Abid Jameel⁴, Islam Zada⁵, Tan N. Nguyen^6,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 135-155, 2024, DOI:10.32604/iasc.2023.041130

Abstract IoT (Internet of Things) devices are being used more and more in a variety of businesses and for a variety of tasks, such as environmental data collection in both civilian and military situations. They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power. In this study, we investigate the possibility of detecting IoT malware using recurrent neural networks (RNNs). RNN is used in the proposed method to investigate the execution operation codes of ARM-based More >

Chaozhi Cai^*, Xiaoyu Guo, Yingfang Xue, Jianhua Ren

Structural Durability & Health Monitoring, Vol.18, No.3, pp. 321-339, 2024, DOI:10.32604/sdhm.2024.045831

Abstract Bleachers play a crucial role in practical engineering applications, and any damage incurred during their operation poses a significant threat to the safety of both life and property. Consequently, it becomes imperative to conduct damage diagnosis and health monitoring of bleachers. The intricate structure of bleachers, the varied types of potential damage, and the presence of similar vibration data in adjacent locations make it challenging to achieve satisfactory diagnosis accuracy through traditional time-frequency analysis methods. Furthermore, field environmental noise can adversely impact the accuracy of bleacher damage diagnosis. To enhance the accuracy and anti-noise capabilities… More > Graphic Abstract

Zhenxiang He^*, Zhenyu Zhao, Ke Chen, Yanlin Liu

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3023-3045, 2024, DOI:10.32604/cmc.2024.050281

Abstract The fast-paced development of blockchain technology is evident. Yet, the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem. Conventional smart contract vulnerability detection primarily relies on static analysis tools, which are less efficient and accurate. Although deep learning methods have improved detection efficiency, they are unable to fully utilize the static relationships within contracts. Therefore, we have adopted the advantages of the above two methods, combining feature extraction mode of tools with deep learning techniques. Firstly, we have constructed corresponding feature extraction mode for… More >

Muhammad Salman Qamar^1,*, Ihsan ul Haq¹, Amil Daraz², Atif M. Alamri³, Salman A. AlQahtani⁴, Muhammad Fahad Munir¹

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2945-2970, 2024, DOI:10.32604/cmc.2024.050168

Abstract In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. In WSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation. WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or pre-determined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs… More >

Di Wang, Yuefei Zhu, Jinlong Fei^*, Maohua Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2253-2276, 2024, DOI:10.32604/cmc.2024.049504

Abstract Website fingerprinting, also known as WF, is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination, even when using the Tor anonymity network. While advanced attacks based on deep neural network (DNN) can perform feature engineering and attain accuracy rates of over 98%, research has demonstrated that DNN is vulnerable to adversarial samples. As a result, many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success. However, these methods suffer from high bandwidth overhead or require access to the target… More >

Yasir Maqsood¹, Syed Muhammad Usman^1,*, Musaed Alhussein², Khursheed Aurangzeb^2,*, Shehzad Khalid³, Muhammad Zubair⁴

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2795-2811, 2024, DOI:10.32604/cmc.2024.049410

Abstract Wheat is a critical crop, extensively consumed worldwide, and its production enhancement is essential to meet escalating demand. The presence of diseases like stem rust, leaf rust, yellow rust, and tan spot significantly diminishes wheat yield, making the early and precise identification of these diseases vital for effective disease management. With advancements in deep learning algorithms, researchers have proposed many methods for the automated detection of disease pathogens; however, accurately detecting multiple disease pathogens simultaneously remains a challenge. This challenge arises due to the scarcity of RGB images for multiple diseases, class imbalance in existing… More >