Nitin Sharma¹, Mohd Anul Haq², Pawan Kumar Dahiya³, B. R. Marwah⁴, Reema Lalit⁵, Nitin Mittal⁶, Ismail Keshta^7,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 881-895, 2023, DOI:10.32604/cmc.2023.027899

Abstract Every developing country relies on transportation, and there has been an exponential expansion in the development of various sorts of vehicles with various configurations, which is a major component strengthening the automobile sector. India is a developing country with increasing road traffic, which has resulted in challenges such as increased road accidents and traffic oversight issues. In the lack of a parametric technique for accurate vehicle recognition, which is a major worry in terms of reliability, high traffic density also leads to mayhem at checkpoints and toll plazas. A system that combines an intelligent domain approach with more sustainability indices… More >

Mohamed A. Meselhi^*, Saber M. Elsayed, Daryl L. Essam, Ruhul A. Sarker

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.027448

Abstract Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be valuable information for the decision-makers.… More >

Lin Zhou^1,*, Xijin Chen¹, Chaoyan Wu¹, Qiuyue Zhong¹, Xu Cheng², Yibin Tang³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1259-1277, 2023, DOI:10.32604/cmc.2023.027379

Abstract Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal ratio masking (IRM) matrix of… More >

Chang Zhang¹, Ruiwen Ni¹, Ye Mu^1,2,3,4, Yu Sun^1,2,3,4,*, Thobela Louis Tyasi⁵

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 983-994, 2023, DOI:10.32604/cmc.2023.027269

Abstract In the field of agricultural information, the identification and prediction of rice leaf disease have always been the focus of research, and deep learning (DL) technology is currently a hot research topic in the field of pattern recognition. The research and development of high-efficiency, high-quality and low-cost automatic identification methods for rice diseases that can replace humans is an important means of dealing with the current situation from a technical perspective. This paper mainly focuses on the problem of huge parameters of the Convolutional Neural Network (CNN) model and proposes a recognition model that combines a multi-scale convolution module with… More >

Borja Bordel^1,*, Ramón Alcarria¹, Joaquin Chung², Rajkumar Kettimuthu², Tomás Robles¹, Iván Armuelles³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 855-880, 2023, DOI:10.32604/cmc.2023.026787

Abstract Future components to enhance the basic, native security of 5G networks are either complex mechanisms whose impact in the requiring 5G communications are not considered, or lightweight solutions adapted to ultra-reliable low-latency communications (URLLC) but whose security properties remain under discussion. Although different 5G network slices may have different requirements, in general, both visions seem to fall short at provisioning secure URLLC in the future. In this work we address this challenge, by introducing cost-security functions as a method to evaluate the performance and adequacy of most developed and employed non-native enhanced security mechanisms in 5G networks. We categorize those… More >

Theerapat Sangpetch¹, Tossapon Boongoen^1,*, Natthakan Iam-On²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1641-1655, 2023, DOI:10.32604/cmc.2023.026739

Abstract Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined… More >

Hyo-Seok Kim¹, Chang-Gyoon Lim², Sang-Joon Lee³, Yong-Min Kim^4,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1749-1763, 2023, DOI:10.32604/cmc.2023.026708

Abstract Recently, Industrial Control Systems (ICSs) have been changing from a closed environment to an open environment because of the expansion of digital transformation, smart factories, and Industrial Internet of Things (IIoT). Since security accidents that occur in ICSs can cause national confusion and human casualties, research on detecting abnormalities by using normal operation data learning is being actively conducted. The single technique proposed by existing studies does not detect abnormalities well or provide satisfactory results. In this paper, we propose a GRU-based Buzzer Ensemble for Abnormal Detection (GBE-AD) model for detecting anomalies in industrial control systems to ensure rapid response… More >

Mazen Mushabab Alqahtani¹, Ashit Kumar Dutta², Sultan Almotairi³, M. Ilayaraja⁴, Amani Abdulrahman Albraikan⁵, Fahd N. Al-Wesabi^6,7,*, Mesfer Al Duhayyim⁸

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 217-233, 2023, DOI:10.32604/cmc.2023.025280

Abstract Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the… More >

Rajesh Singh¹, Anita Gehlot¹, Ritika Saxena², Khalid Alsubhi³, Divya Anand^1,*, Irene Delgado Noya^4,5, Shaik Vaseem Akram¹, Sushabhan Choudhury²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1217-1233, 2023, DOI:10.32604/cmc.2023.023894

Abstract Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic skin reaction (GSR) sensor is… More >

Edgar Scavino^1,*, Mohd Amiruddin Abd Rahman¹, Zahid Farid²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 379-397, 2023, DOI:10.32604/cmc.2023.023824

Abstract Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received… More >