Yaqiong He, Jinlin Fan^*, Huaiguang Wu

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 995-1032, 2024, DOI:10.32604/cmc.2024.052887

Abstract With the rise of blockchain technology, the security issues of smart contracts have become increasingly critical. Despite the availability of numerous smart contract vulnerability detection tools, many face challenges such as slow updates, usability issues, and limited installation methods. These challenges hinder the adoption and practicality of these tools. This paper examines smart contract vulnerability detection tools from 2016 to 2023, sourced from the Web of Science (WOS) and Google Scholar. By systematically collecting, screening, and synthesizing relevant research, 38 open-source tools that provide installation methods were selected for further investigation. From a developer’s perspective,… More >

Yifei Zhao, Zhaohui Li, Siyi Lv^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 217-234, 2024, DOI:10.32604/cmc.2024.052310

Abstract The EU’s Artificial Intelligence Act (AI Act) imposes requirements for the privacy compliance of AI systems. AI systems must comply with privacy laws such as the GDPR when providing services. These laws provide users with the right to issue a Data Subject Access Request (DSAR). Responding to such requests requires database administrators to identify information related to an individual accurately. However, manual compliance poses significant challenges and is error-prone. Database administrators need to write queries through time-consuming labor. The demand for large amounts of data by AI systems has driven the development of NoSQL databases.… More >

Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1457-1490, 2024, DOI:10.32604/cmc.2024.051996

Abstract This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing intrusion detection performance, given the vital relevance of safeguarding computer networks against harmful activity. The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset, a popular benchmark for IDS research. The model performs well in both the training and validation stages, with 91.30% training accuracy and 94.38% validation accuracy. Thus, the model shows good learning and generalization capabilities with minor losses of… More >

Mohamed Karray^1,*, Nesrine Triki^2,*, Mohamed Ksantini²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 119-138, 2024, DOI:10.32604/cmc.2024.051562

Abstract Advanced Driver Assistance Systems (ADAS) technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road. Traffic Sign Recognition System (TSRS) is one of the most important components of ADAS. Among the challenges with TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time. Accordingly, this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules. Firstly, the Speed Limit Detection (SLD) module uses… More >

Anas AbuKaraki¹, Tawfi Alrawashdeh¹, Sumaya Abusaleh¹, Malek Zakarya Alksasbeh^1,*, Bilal Alqudah¹, Khalid Alemerien², Hamzah Alshamaseen³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Xu Zhang¹, Yi He¹, Haiwen Yi¹, Yulu Zhang², Yuan Li², Shuai Ma², Gui Li³, Zhiyuan Zhao⁴, Yue Liu¹, Junyang Liu¹, Guangjun Wen¹, Jian Li^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 897-912, 2024, DOI:10.32604/cmc.2024.050000

Abstract When the radio frequency identification (RFID) system inventories multiple tags, the recognition rate will be seriously affected due to collisions. Based on the existing dynamic frame slotted Aloha (DFSA) algorithm, a sub-frame observation and cyclic redundancy check (CRC) grouping combined dynamic framed slotted Aloha (SUBF-CGDFSA) algorithm is proposed. The algorithm combines the precise estimation method of the quantity of large-scale tags, the large-scale tags grouping mechanism based on CRC pseudo-random characteristics, and the Aloha anti-collision optimization mechanism based on sub-frame observation. By grouping tags and sequentially identifying them within subframes, it accurately estimates the number More >

Shazielya Shamsul¹, Mohammed A. H. Ali^1,2,*, Salwa Hanim Abdul-Rashid^1,2, Atef M. Ghaleb³, Fahad M. Alqahtani⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1277-1304, 2024, DOI:10.32604/cmc.2024.049257

Abstract This paper presents a smart checkout system designed to mitigate the issues of noise and errors present in the existing barcode and RFID-based systems used at retail stores’ checkout counters. This is achieved by integrating a novel AI algorithm, called Improved Laser Simulator Logic (ILSL) into the RFID system. The enhanced RFID system was able to improve the accuracy of item identification, reduce noise interference, and streamline the overall checkout process. The potential of the system for noise detection and elimination was initially investigated through a simulation study using MATLAB and ILSL algorithm. Subsequently, it More >

Ezhilmathi Nagarathinam¹, Buvana Devaraju², Karthiyayini Jayamoorthy³, Padmavathi Radhakrishnan⁴, Santhana Lakshmi ChandraMohan⁵, Vijayakumar Perumal⁶, Karthikeyan Balakrishnan^7,*

Energy Engineering, Vol.121, No.8, pp. 2129-2142, 2024, DOI:10.32604/ee.2024.052280

Abstract Maximum Power Point Tracking (MPPT) is crucial for maximizing the energy output of photovoltaic (PV) systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental conditions. This work proposes the design of an MPPT system for solar PV installations using the Differential Grey Wolf Optimizer (DGWO). It dynamically adjusts the parameters of the MPPT controller, specifically the duty cycle of the SEPIC converter, to efficiently track the Maximum Power Point (MPP). The proposed system aims to enhance the energy harvesting capability of solar PV More >