Noor Gul^1,2, Saeed Ahmed^1,3, Su Min Kim¹, Muhammad Sajjad Khan⁴, Junsu Kim^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5209-5227, 2023, DOI:10.32604/cmc.2023.026945

Abstract A cognitive radio network (CRN) intelligently utilizes the available spectral resources by sensing and learning from the radio environment to maximize spectrum utilization. In CRNs, the secondary users (SUs) opportunistically access the primary users (PUs) spectrum. Therefore, unambiguous detection of the PU channel occupancy is the most critical aspect of the operations of CRNs. Cooperative spectrum sensing (CSS) is rated as the best choice for making reliable sensing decisions. This paper employs machine-learning tools to sense the PU channels reliably in CSS. The sensing parameters are reconfigured to maximize the spectrum utilization while reducing sensing error and cost with improved… More >

Mahima Dahiya^*, Nitin Nitin

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430

Abstract Intrusion detection is critical to guaranteeing the safety of the data in the network. Even though, since Internet commerce has grown at a breakneck pace, network traffic kinds are rising daily, and network behavior characteristics are becoming increasingly complicated, posing significant hurdles to intrusion detection. The challenges in terms of false positives, false negatives, low detection accuracy, high running time, adversarial attacks, uncertain attacks, etc. lead to insecure Intrusion Detection System (IDS). To offset the existing challenge, the work has developed a secure Data Mining Intrusion detection system (DataMIDS) framework using Functional Perturbation (FP) feature selection and Bengio Nesterov Momentum-based… More >

Yinggang Sun¹, Hongguo Zhang¹, Chao Ma^1,*, Hai Huang¹, Dongyang Zhan^2,3, Jiaxing Qu⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4043-4058, 2023, DOI:10.32604/cmc.2023.031684

Abstract The problem of data island hinders the application of big data in artificial intelligence model training, so researchers propose a federated learning framework. It enables model training without having to centralize all data in a central storage point. In the current horizontal federated learning scheme, each participant gets the final jointly trained model. No solution is proposed for scenarios where participants only provide training data in exchange for benefits, but do not care about the final jointly trained model. Therefore, this paper proposes a new boosted tree algorithm, called RPBT (the originator Rights Protected federated Boosted Tree algorithm). Compared with… More >

A. Reyana^1,*, P. Vijayalakshmi²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1013-1025, 2023, DOI:10.32604/iasc.2023.032538

Abstract Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. Thus to predict the occurrence… More >

Mohammad Al-Omari¹, Fadi Qutaishat¹, Majdi Rawashdeh¹, Samah H. Alajmani², Mehedi Masud^3,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 515-527, 2023, DOI:10.32604/iasc.2023.030374

Abstract Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested on a real-world dataset of… More >

S. Sadhasivam¹, P. Valarmathie², K. Dinakaran^3,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 461-479, 2023, DOI:10.32604/iasc.2023.028650

Abstract With the vast advancements in Information Technology, the emergence of Online Social Networking (OSN) has also hit its peak and captured the attention of the young generation people. The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user. However, the attackers also target this height of OSN utilization, explicitly creating the clones of the user’s account. Various clone detection mechanisms are designed based on social-network activities. For instance, monitoring the occurrence of clone edges is done to restrict the generation of clone activities. However, this assumption is unsuitable… More >

Hasan H. Al-Khshali^1,*, Muhammad Ilyas²

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 153-178, 2023, DOI:10.32604/cmc.2023.032182

Abstract One aspect of cybersecurity, incorporates the study of Portable Executables (PE) files maleficence. Artificial Intelligence (AI) can be employed in such studies, since AI has the ability to discriminate benign from malicious files. In this study, an exclusive set of 29 features was collected from trusted implementations, this set was used as a baseline to analyze the presented work in this research. A Decision Tree (DT) and Neural Network Multi-Layer Perceptron (NN-MLPC) algorithms were utilized during this work. Both algorithms were chosen after testing a few diverse procedures. This work implements a method of subgrouping features to answer questions such… More >

Aqsa Aqdus¹, Rashid Amin^1,*, Sadia Ramzan¹, Sultan S. Alshamrani², Abdullah Alshehri³, El-Sayed M. El-kenawy⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1413-1435, 2023, DOI:10.32604/cmc.2023.031719

Abstract The rapid advancement of wireless communication is forming a hyper-connected 5G network in which billions of linked devices generate massive amounts of data. The traffic control and data forwarding functions are decoupled in software-defined networking (SDN) and allow the network to be programmable. Each switch in SDN keeps track of forwarding information in a flow table. The SDN switches must search the flow table for the flow rules that match the packets to handle the incoming packets. Due to the obvious vast quantity of data in data centres, the capacity of the flow table restricts the data plane’s forwarding capabilities.… More >

Hongfei Ma^1,*, Wenqi Zhao², Yurong Zhao¹, Yu He¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1773-1790, 2023, DOI:10.32604/cmes.2022.020498

Abstract Accurate prediction of monthly oil and gas production is essential for oil enterprises to make reasonable production plans, avoid blind investment and realize sustainable development. Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise, and the application conditions are very demanding. With the rapid development of artificial intelligence technology, big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development. Based on the data-driven artificial intelligence algorithm Gradient Boosting Decision Tree (GBDT), this paper predicts the initial single-layer production by considering geological data, fluid PVT… More >

M. Valavan, S. Rita^*

Computer Systems Science and Engineering, Vol.45, No.1, pp. 231-245, 2023, DOI:10.32604/csse.2023.026508

Abstract Fraud detection for credit/debit card, loan defaulters and similar types is achievable with the assistance of Machine Learning (ML) algorithms as they are well capable of learning from previous fraud trends or historical data and spot them in current or future transactions. Fraudulent cases are scant in the comparison of non-fraudulent observations, almost in all the datasets. In such cases detecting fraudulent transaction are quite difficult. The most effective way to prevent loan default is to identify non-performing loans as soon as possible. Machine learning algorithms are coming into sight as adept at handling such data with enough computing influence.… More >