Abdul Sattar Palli^1,6,*, Jafreezal Jaafar^1,2, Manzoor Ahmed Hashmani^1,3, Heitor Murilo Gomes^4,5, Aeshah Alsughayyir⁷, Abdul Rehman Gilal¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1827-1845, 2023, DOI:10.32604/cmc.2023.033934

Abstract Every application in a smart city environment like the smart grid, health monitoring, security, and surveillance generates non-stationary data streams. Due to such nature, the statistical properties of data changes over time, leading to class imbalance and concept drift issues. Both these issues cause model performance degradation. Most of the current work has been focused on developing an ensemble strategy by training a new classifier on the latest data to resolve the issue. These techniques suffer while training the new classifier if the data is imbalanced. Also, the class imbalance ratio may change greatly from one input stream to another,… More >

Xiaoming Tian^*, Guangfeng Xiang, Hao Lv, Jing Peng, Lu Zhu

Phyton-International Journal of Experimental Botany, Vol.92, No.3, pp. 837-850, 2023, DOI:10.32604/phyton.2023.024408

Abstract In the present study, the variation patterns of leaf shape in different populations of individual Semiliquidambar cathayensis plants were analyzed to investigate the relationship among leaf shape variation, photosynthetic properties, and active compounds to understand the genetic characteristics of S. cathayensis and screen elite germplasms. The leaf shape of 18 offspring from three natural S. cathayensis populations was analyzed to investigate the level of diversity and variation patterns of leaf shape. Furthermore, photosynthetic pigment content, physiological parameters of photosynthesis, and the active compounds in leaves of different shapes were determined. Statistical analysis showed that the leaf shape variation in  S. cathayensis… More >

Abdullah M. Basahel¹, Mohammad Yamin¹, Sulafah M. Basahel², E. Laxmi Lydia^3,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4319-4336, 2023, DOI:10.32604/cmc.2023.033497

Abstract Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. Intrusion Detection System (IDS) refers to one of the commonly utilized system for detecting attacks on cloud. IDS proves to be an effective and promising technique, that identifies malicious activities and known threats by observing traffic data in computers, and warnings are given when such threats were identified. The current mainstream IDS are assisted… More >

Poonam^*, Suman Sangwan

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1783-1799, 2023, DOI:10.32604/cmc.2023.031729

Abstract This paper presents a novel fuzzy firefly-based intelligent algorithm for load balancing in mobile cloud computing while reducing makespan. The proposed technique implicitly acts intelligently by using inherent traits of fuzzy and firefly. It automatically adjusts its behavior or converges depending on the information gathered during the search process and objective function. It works for 3-tier architecture, including cloudlet and public cloud. As cloudlets have limited resources, fuzzy logic is used for cloudlet selection using capacity and waiting time as input. Fuzzy provides human-like decisions without using any mathematical model. Firefly is a powerful meta-heuristic optimization technique to balance diversification… More >

Gürcan Çetin^*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4519-4539, 2022, DOI:10.32604/cmc.2022.031734

Abstract Recently, machine learning algorithms have been used in the detection and classification of network attacks. The performance of the algorithms has been evaluated by using benchmark network intrusion datasets such as DARPA98, KDD’99, NSL-KDD, UNSW-NB15, and Caida DDoS. However, these datasets have two major challenges: imbalanced data and high-dimensional data. Obtaining high accuracy for all attack types in the dataset allows for high accuracy in imbalanced datasets. On the other hand, having a large number of features increases the runtime load on the algorithms. A novel model is proposed in this paper to overcome these two concerns. The number of… More >

Jyoti Arora¹, Meena Tushir², Keshav Sharma¹, Lalit Mohan¹, Aman Singh^3,*, Abdullah Alharbi⁴, Wael Alosaimi⁴

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4801-4817, 2022, DOI:10.32604/cmc.2022.025960

Abstract Datasets with the imbalanced class distribution are difficult to handle with the standard classification algorithms. In supervised learning, dealing with the problem of class imbalance is still considered to be a challenging research problem. Various machine learning techniques are designed to operate on balanced datasets; therefore, the state of the art, different under-sampling, over-sampling and hybrid strategies have been proposed to deal with the problem of imbalanced datasets, but highly skewed datasets still pose the problem of generalization and noise generation during resampling. To over-come these problems, this paper proposes a majority clustering model for classification of imbalanced datasets known… More >

Mini Prince^1,*, P. M. Joe Prathap²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1009-1024, 2023, DOI:10.32604/csse.2023.024277

Abstract Most modern technologies, such as social media, smart cities, and the internet of things (IoT), rely on big data. When big data is used in the real-world applications, two data challenges such as class overlap and class imbalance arises. When dealing with large datasets, most traditional classifiers are stuck in the local optimum problem. As a result, it’s necessary to look into new methods for dealing with large data collections. Several solutions have been proposed for overcoming this issue. The rapid growth of the available data threatens to limit the usefulness of many traditional methods. Methods such as oversampling and… More >

Rasha Almarshdi^1,2,*, Laila Nassef¹, Etimad Fadel¹, Nahed Alowidi¹

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 297-320, 2023, DOI:10.32604/iasc.2023.026799

Abstract Internet of Things (IoT) is the most widespread and fastest growing technology today. Due to the increasing of IoT devices connected to the Internet, the IoT is the most technology under security attacks. The IoT devices are not designed with security because they are resource constrained devices. Therefore, having an accurate IoT security system to detect security attacks is challenging. Intrusion Detection Systems (IDSs) using machine learning and deep learning techniques can detect security attacks accurately. This paper develops an IDS architecture based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) deep learning algorithms. We implement our model… More >

T. Ragupathi^1,*, M. Govindarajan¹, T. Priyaradhikadevi²

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1351-1366, 2022, DOI:10.32604/iasc.2022.025756

Abstract The rapid advancements in the field of big data, wearables, Internet of Things (IoT), connected devices, and cloud environment find useful to improve the quality of healthcare services. Medical data classification using the data collected by the wearables and IoT devices can be used to determine the presence or absence of disease. The recently developed deep learning (DL) models can be used for several processes such as classification, natural language processing, etc. This study presents a bacterial foraging optimization (BFO) based convolutional neural network-gated recurrent unit (CNN-GRU) with class imbalance handling (CIH) model, named BFO-CNN-GRU-CIH for medical data classification in… More >

Heewon Chung, Jinseok Lee^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5607-5628, 2022, DOI:10.32604/cmc.2022.028154

Abstract For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training. Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class. To alleviate such class imbalance, semi-supervised learning methods using additional unlabeled data have been considered. However, as a matter of course, the accuracy is much lower than that from supervised learning. In this study, under the assumption that additional unlabeled data is available, we propose the iterative semi-supervised learning algorithms, which… More >