Manal Abdullah Alohali¹, Muna Elsadig¹, Fahd N. Al-Wesabi², Mesfer Al Duhayyim³, Anwer Mustafa Hilal^4,*, Abdelwahed Motwakel⁴

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3087-3102, 2023, DOI:10.32604/csse.2023.036802

Abstract With the advent of the Internet of Things (IoT), several devices like sensors nowadays can interact and easily share information. But the IoT model is prone to security concerns as several attackers try to hit the network and make it vulnerable. In such scenarios, security concern is the most prominent. Different models were intended to address these security problems; still, several emergent variants of botnet attacks like Bashlite, Mirai, and Persirai use security breaches. The malware classification and detection in the IoT model is still a problem, as the adversary reliably generates a new variant of IoT malware and actively… More >

Saud Yonbawi¹, Sultan Alahmari², T. Satyanarayana murthy³, Ravuri Daniel⁴, E. Laxmi Lydia⁵, Mohamad Khairi Ishak⁶, Hend Khalid Alkahtani^7,*, Ayman Aljarbouh⁸, Samih M. Mostafa⁹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3847-3864, 2023, DOI:10.32604/csse.2023.036552

Abstract Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification… More >

Chandana Gouri Tekkali, Karthika Natarajan^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3171-3187, 2023, DOI:10.32604/cmc.2023.036865

Abstract Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features… More >

Dongyang Li¹, Shiyu Du^2,*, Yiming Zhang², Meiting Zhao³

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2775-2803, 2023, DOI:10.32604/cmc.2023.035911

Abstract Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s… More >

Adepu Shravan Kumar, S. Srinivasan^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3607-3620, 2023, DOI:10.32604/iasc.2023.036647

Abstract At the present time, the Industrial Internet of Things (IIoT) has swiftly evolved and emerged, and picture data that is collected by terminal devices or IoT nodes are tied to the user's private data. The use of image sensors as an automation tool for the IIoT is increasingly becoming more common. Due to the fact that this organisation transfers an enormous number of photographs at any one time, one of the most significant issues that it has is reducing the total quantity of data that is sent and, as a result, the available bandwidth, without compromising the image quality. Image… More >

Manar Ahmed Hamza^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719

Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the way for highly intellectual and… More >

Ierin Babu^1,*, R. MathuSoothana², S. Kumar²

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3621-3636, 2023, DOI:10.32604/iasc.2023.033791

Abstract Educational Data Mining (EDM) is an emergent discipline that concentrates on the design of self-learning and adaptive approaches. Higher education institutions have started to utilize analytical tools to improve students’ grades and retention. Prediction of students’ performance is a difficult process owing to the massive quantity of educational data. Therefore, Artificial Intelligence (AI) techniques can be used for educational data mining in a big data environment. At the same time, in EDM, the feature selection process becomes necessary in creation of feature subsets. Since the feature selection performance affects the predictive performance of any model, it is important to elaborately… More >

Yuxin Chen¹, Weixun Yong¹, Chuanqi Li², Jian Zhou^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2507-2526, 2023, DOI:10.32604/cmes.2023.025714

Abstract After the excavation of the roadway, the original stress balance is destroyed, resulting in the redistribution of stress and the formation of an excavation damaged zone (EDZ) around the roadway. The thickness of EDZ is the key basis for roadway stability discrimination and support structure design, and it is of great engineering significance to accurately predict the thickness of EDZ. Considering the advantages of machine learning (ML) in dealing with high-dimensional, nonlinear problems, a hybrid prediction model based on the random forest (RF) algorithm is developed in this paper. The model used the dragonfly algorithm (DA) to optimize two hyperparameters… More >

Salahahaldeen Duraibi^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2509-2525, 2023, DOI:10.32604/csse.2023.036119

Abstract With the growth of the discipline of digital communication, the topic has acquired more attention in the cybersecurity medium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)—is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the… More >

Anwer Mustafa Hilal^1,*, Fadwa Alrowais², Fahd N. Al-Wesabi³, Radwa Marzouk^4,5

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1929-1945, 2023, DOI:10.32604/csse.2023.035529

Abstract The problem of producing a natural language description of an image for describing the visual content has gained more attention in natural language processing (NLP) and computer vision (CV). It can be driven by applications like image retrieval or indexing, virtual assistants, image understanding, and support of visually impaired people (VIP). Though the VIP uses other senses, touch and hearing, for recognizing objects and events, the quality of life of those persons is lower than the standard level. Automatic Image captioning generates captions that will be read loudly to the VIP, thereby realizing matters happening around them. This article introduces… More >