Shilpa Suman¹, Abhishek Rawat^2,*, Anil Kumar³, S. K. Tiwari⁴

Revue Internationale de Géomatique, Vol.33, pp. 363-381, 2024, DOI:10.32604/rig.2024.053981

Abstract In this study, the impact of the training sample selection method on the performance of fuzzy-based Possibilistic c-means (PCM) and Noise Clustering (NC) classifiers were examined and mapped the cumin and fennel rabi crop. Two training sample selection approaches that have been investigated in this study are “mean” and “individual sample as mean”. Both training sample techniques were applied to the PCM and NC classifiers to classify the two indices approach. Both approaches have been studied to decrease spectral information in temporal data processing. The Modified Soil Adjusted Vegetation Index 2 (MSAVI-2) and Class-Based Sensor… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.10, pp. 1691-1692, 2024, DOI:10.32604/or.2024.056908

Abstract This article has no abstract. More >

Raed Alotaibi¹, Omar Reyad^2,3, Mohamed Esmail Karar^4,*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1133-1147, 2024, DOI:10.32604/csse.2024.053358

Abstract E-learning behavior data indicates several students’ activities on the e-learning platform such as the number of accesses to a set of resources and number of participants in lectures. This article proposes a new analytics system to support academic evaluation for students via e-learning activities to overcome the challenges faced by traditional learning environments. The proposed e-learning analytics system includes a new deep forest model. It consists of multistage cascade random forests with minimal hyperparameters compared to traditional deep neural networks. The developed forest model can analyze each student’s activities during the use of an e-learning… More >

Jyun-Guo Wang^*

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1149-1170, 2024, DOI:10.32604/csse.2024.052931

Abstract In many Eastern and Western countries, falling birth rates have led to the gradual aging of society. Older adults are often left alone at home or live in a long-term care center, which results in them being susceptible to unsafe events (such as falls) that can have disastrous consequences. However, automatically detecting falls from video data is challenging, and automatic fall detection methods usually require large volumes of training data, which can be difficult to acquire. To address this problem, video kinematic data can be used as training data, thereby avoiding the requirement of creating… More >

Mohammad Ababneh^*, Ayat Al-Droos, Ammar El-Hassan

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1171-1191, 2024, DOI:10.32604/csse.2024.052875

Abstract With the high level of proliferation of connected mobile devices, the risk of intrusion becomes higher. Artificial Intelligence (AI) and Machine Learning (ML) algorithms started to feature in protection software and showed effective results. These algorithms are nonetheless hindered by the lack of rich datasets and compounded by the appearance of new categories of malware such that the race between attackers’ malware, especially with the assistance of Artificial Intelligence tools and protection solutions makes these systems and frameworks lose effectiveness quickly. In this article, we present a framework for mobile malware detection based on a… More >

Amita U. Dessai^*, Hassanali G. Virani

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1193-1211, 2024, DOI:10.32604/csse.2024.052710

Abstract Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction (HCI). However, physical methods of emotion recognition such as facial expressions, voice, and text data, do not always indicate true emotions, as users can falsify them. Among the physiological methods of emotion detection, Electrocardiogram (ECG) is a reliable and efficient way of detecting emotions. ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments. Researchers use deep machine learning techniques for emotion recognition using ECG signals, but there is a need to develop efficient models… More >

Mohammad Javad Shayegan^*, Rosa Akhtari

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1251-1272, 2024, DOI:10.32604/csse.2024.052587

Abstract After the spread of COVID-19, e-learning systems have become crucial tools in educational systems worldwide, spanning all levels of education. This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data, making it an attractive resource for predicting student performance. In this study, we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets. The stacking method was employed for modeling in this research. The proposed model utilized weak learners, including nearest neighbor, decision tree, random forest, enhanced gradient, simple Bayes, More >

Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Ayman Yafoz⁴, Mahmoud Othman⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.48, No.5, pp. 1387-1403, 2024, DOI:10.32604/csse.2023.033902

Abstract Handwritten character recognition becomes one of the challenging research matters. More studies were presented for recognizing letters of various languages. The availability of Arabic handwritten characters databases was confined. Almost a quarter of a billion people worldwide write and speak Arabic. More historical books and files indicate a vital data set for many Arab nations written in Arabic. Recently, Arabic handwritten character recognition (AHCR) has grabbed the attention and has become a difficult topic for pattern recognition and computer vision (CV). Therefore, this study develops fireworks optimization with the deep learning-based AHCR (FWODL-AHCR) technique. The… More >

Emre Ozsahin^1,*, Huseyin Sarı², Duygu Boyraz Erdem², Mikayil Ozturk²

Revue Internationale de Géomatique, Vol.33, pp. 341-361, 2024, DOI:10.32604/rig.2024.054197

Abstract This study assessed and mapped the spatial distribution of soil types and properties developed under the forest cover of the Strandzha Mountains of Türkiye. The study was conducted on a micro-scale in the riparian zone of the Balaban River, which characterizes the soils distributed in the mountainous area. The effect of environmental factors on the spatial distribution of soil types and properties was also determined. To gather data, soil sampling, laboratory analysis, data processing and mapping were sequentially performed. These data were analyzed using the Geographical Information System (GIS) based Random Forest (RF) machine learning… More > Graphic Abstract

Youqi Jiang^1,#, Bing Wu^2,#, Jiahui Chen³, Ruyi Jin³, Guangshan Jin³, Minhao Zhang⁴, Qin Zhou^4,*, Aiji Jiang^2,*

Psycho-Oncologie, Vol.18, No.3, pp. 191-200, 2024, DOI:10.32604/po.2024.053942

Abstract Objective: This study evaluated the effect of resilience on depression among patients with breast cancer from individual and familial perspectives by exploring the mediating and moderating effects of family resilience between individual resilience and depression. Methods: A questionnaire survey was conducted among 337 patients with breast cancer who were admitted to the Oncology Department of Jiangsu Province Hospital. The survey included demographic information, the Connor–Davidson Resilience Scale (CD-RISC), the Family Resilience Assessment Scale (FRAS), and the Chinese version of the Patient Health Questionnaire-9 (PHQ-9) for Depression. The relationship among individual resilience, family resilience, and depression… More >