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Varada Rajkumar Kukkala¹, Surapaneni Phani Praveen², Naga Satya Koti Mani Kumar Tirumanadham³, Parvathaneni Naga Srinivasu^4,5,*
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1085-1112, 2024, DOI:10.32604/csse.2024.053603
Abstract This paper investigates the application of machine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely; Z-Score incorporated with Grey Wolf Optimization (GWO) as well as Interquartile Range (IQR) coupled with Ant Colony Optimization (ACO). Using a performance index, it is shown that when compared with the Z-Score and GWO with AdaBoost, the IQR and ACO, with AdaBoost are not very accurate (89.0% vs. 86.0%) and less discriminative (Area Under the Curve (AUC) score of 93.0% vs. 91.0%). The Z-Score and GWO… More >

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Zakir Hussain Ahmed^1,*, Maha Ata Al-Furhood², Abdul Khader Jilani Saudagar³, Shakir Khan⁴
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1113-1131, 2024, DOI:10.32604/csse.2024.053574
（This article belongs to the Special Issue: Metaheuristics Optimization for Real-World Applications)
Abstract The generalized travelling salesman problem (GTSP), a generalization of the well-known travelling salesman problem (TSP), is considered for our study. Since the GTSP is NP-hard and very complex, finding exact solutions is highly expensive, we will develop genetic algorithms (GAs) to obtain heuristic solutions to the problem. In GAs, as the crossover is a very important process, the crossover methods proposed for the traditional TSP could be adapted for the GTSP. The sequential constructive crossover (SCX) and three other operators are adapted to use in GAs to solve the GTSP. The effectiveness of GA using More >

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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 >

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71

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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 >

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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 >

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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
（This article belongs to the Special Issue: Deep Learning and Ontology in Smart Healthcare)
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 >

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Soha A. Shaban^*, Amira A. Atta, Dalia L. Elsheweikh
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1213-1250, 2024, DOI:10.32604/csse.2024.052603
Abstract Field training is the backbone of the teacher-preparation process. Its importance stems from the goals that colleges of education aim to achieve, which include bridging the gap between theory and practice and aligning with contemporary educational trends during teacher training. Currently, trainee students attendance in field training is recorded manually through signatures on attendance sheets. However, this method is prone to impersonation, time wastage, and misplacement. Additionally, traditional methods of evaluating trainee students are often susceptible to human errors during the evaluation and scoring processes. Field training also lacks modern technology that the supervisor can… More >

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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 >

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Talal H. Noor^*
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1273-1299, 2024, DOI:10.32604/csse.2024.052510
Abstract In recent years, container-based cloud virtualization solutions have emerged to mitigate the performance gap between non-virtualized and virtualized physical resources. However, there is a noticeable absence of techniques for predicting microservice performance in current research, which impacts cloud service users’ ability to determine when to provision or de-provision microservices. Predicting microservice performance poses challenges due to overheads associated with actions such as variations in processing time caused by resource contention, which potentially leads to user confusion. In this paper, we propose, develop, and validate a probabilistic architecture named Microservice Performance Diagnosis and Prediction (MPDP). MPDP… More >

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Qiping Zou¹, Zhong Ruan^2,*, Huaning Song¹
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1301-1328, 2024, DOI:10.32604/csse.2024.051796
（This article belongs to the Special Issue: Security and Privacy in Cognitive Internet of Vehicle)
Abstract As an important application of intelligent transportation system, Internet of Vehicles (IoV) provides great convenience for users. Users can obtain real-time traffic conditions through the IoV’s services, plan users' travel routes, and improve travel efficiency. However, in the IoV system, there are always malicious vehicle nodes publishing false information. Therefore, it is essential to ensure the legitimacy of the source. In addition, during the peak period of vehicle travel, the vehicle releases a large number of messages, and IoV authentication efficiency is prone to performance bottlenecks. Most existing authentication schemes have the problem of low… More >

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Guangbing Xiao^*, Ruijie Gu, Ning Sun, Yong Zhang
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1329-1348, 2024, DOI:10.32604/csse.2024.050817
Abstract In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems, this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis (PCA) and Dual-Heap Filtering (DHF). The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching, which significantly reduces computational complexity. To ensure the accuracy of feature matching, the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs. To further More >

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Robertas Damasevicius^1,*, Faheem Mahmood², Yaseen Zaman³, Sobia Dastgeer², Sajid Khan²
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1349-1366, 2024, DOI:10.32604/csse.2024.050359
Abstract Plant diseases must be identified as soon as possible since they have an impact on the growth of the corresponding species. Consequently, the identification of leaf diseases is essential in this field of agriculture. Diseases brought on by bacteria, viruses, and fungi are a significant factor in reduced crop yields. Numerous machine learning models have been applied in the identification of plant diseases, however, with the recent developments in deep learning, this field of study seems to hold huge potential for improved accuracy. This study presents an effective method that uses image processing and deep… More >

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P. K. A. Chitra¹, S. Appavu alias Balamurugan², S. Geetha³, Seifedine Kadry^4,5,6, Jungeun Kim^7,*, Keejun Han⁸
Computer Systems Science and Engineering, Vol.48, No.5, pp. 1367-1385, 2024, DOI:10.32604/csse.2023.034373
Abstract A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l₂₁ norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed… More >

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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 >

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