A. Manju¹, R. kaladevi², Shanmugasundaram Hariharan³, Shih-Yu Chen^4,5,*, Vinay Kukreja⁶, Pradip Kumar Sharma⁷, Fayez Alqahtani⁸, Amr Tolba⁹, Jin Wang¹⁰

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 993-1007, 2023, DOI:10.32604/cmc.2023.039567 - 08 June 2023

Abstract The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from… More >

Sait Can Yucebas^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 49-71, 2023, DOI:10.32604/iasc.2023.036871 - 29 April 2023

Abstract The number of studies in the literature that diagnose cancer with machine learning using genome data is quite limited. These studies focus on the prediction performance, and the extraction of genomic factors that cause disease is often overlooked. However, finding underlying genetic causes is very important in terms of early diagnosis, development of diagnostic kits, preventive medicine, etc. The motivation of our study was to diagnose bladder cancer (BCa) based on genetic data and to reveal underlying genetic factors by using machine-learning models. In addition, conducting hyper-parameter optimization to get the best performance from different… More >

Feng Zhao^1,2, Di Liao^1,*, Xiaoqiang Chen^1,2, Ying Wang^1,2

Energy Engineering, Vol.120, No.5, pp. 1133-1148, 2023, DOI:10.32604/ee.2023.025900 - 20 February 2023

Abstract Aiming at the problems of multiple types of power quality composite disturbances, strong feature correlation and high recognition error rate, a method of power quality composite disturbances identification based on multi-resolution S-transform and decision tree was proposed. Firstly, according to IEEE standard, the signal models of seven single power quality disturbances and 17 combined power quality disturbances are given, and the disturbance waveform samples are generated in batches. Then, in order to improve the recognition accuracy, the adjustment factor is introduced to obtain the controllable time-frequency resolution through multi-resolution S-transform time-frequency domain analysis. On this… More >

Manar Mohamed Hafez^1,*, Essam Eldin F. Elfakharany¹, Amr A. Abohany², Mostafa Thabet³

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 943-958, 2023, DOI:10.32604/cmc.2023.034078 - 06 February 2023

Abstract Big data is usually unstructured, and many applications require the analysis in real-time. Decision tree (DT) algorithm is widely used to analyze big data. Selecting the optimal depth of DT is time-consuming process as it requires many iterations. In this paper, we have designed a modified version of a (DT). The tree aims to achieve optimal depth by self-tuning running parameters and improving the accuracy. The efficiency of the modified (DT) was verified using two datasets (airport and fire datasets). The airport dataset has 500000 instances and the fire dataset has 600000 instances. A comparison More >

Hameedur Rahman¹, Junaid Tariq^2,*, M. Ali Masood¹, Ahmad F. Subahi³, Osamah Ibrahim Khalaf⁴, Youseef Alotaibi⁵

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5527-5543, 2023, DOI:10.32604/cmc.2023.033190 - 28 December 2022

Abstract Sentiment Analysis (SA) is often referred to as opinion mining. It is defined as the extraction, identification, or characterization of the sentiment from text. Generally, the sentiment of a textual document is classified into binary classes i.e., positive and negative. However, fine-grained classification provides a better insight into the sentiments. The downside is that fine-grained classification is more challenging as compared to binary. On the contrary, performance deteriorates significantly in the case of multi-class classification. In this study, pre-processing techniques and machine learning models for the multi-class classification of sentiments were explored. To augment the… More >

Yichiet Aun^1,*, Ming-Lee Gan¹, Nur Haliza Binti Abdul Wahab², Goh Hock Guan¹

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 4917-4931, 2023, DOI:10.32604/cmc.2023.032373 - 28 December 2022

Abstract In defense-in-depth, humans have always been the weakest link in cybersecurity. However, unlike common threats, social engineering poses vulnerabilities not directly quantifiable in penetration testing. Most skilled social engineers trick users into giving up information voluntarily through attacks like phishing and adware. Social Engineering (SE) in social media is structurally similar to regular posts but contains malicious intrinsic meaning within the sentence semantic. In this paper, a novel SE model is trained using a Recurrent Neural Network Long Short Term Memory (RNN-LSTM) to identify well-disguised SE threats in social media posts. We use a custom… More >

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 - 28 December 2022

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 More >

Mahima Dahiya^*, Nitin Nitin

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4183-4201, 2023, DOI:10.32604/cmc.2023.032430 - 31 October 2022

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… 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 - 31 October 2022

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… More >

A. Reyana^1,*, P. Vijayalakshmi²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 1013-1025, 2023, DOI:10.32604/iasc.2023.032538 - 29 September 2022

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. More >