PeerJ Computer Science最新文献

{"title":"Level up your coding: a systematic review of personalized, cognitive, and gamified learning in programming education.","authors":"Kashif Ishaq, Atif Alvi, Muhammad Ikram Ul Haq, Fadhilah Rosdi, Abubakar Nazeer Choudhry, Arslan Anjum, Fawad Ali Khan","doi":"10.7717/peerj-cs.2310","DOIUrl":"10.7717/peerj-cs.2310","url":null,"abstract":"<p><p>Programming courses in computer science play a crucial role as they often serve as students' initial exposure to computer programming. Many university students find introductory courses overwhelming due to the vast amount of information they need to grasp. The traditional teacher-lecturer model used in university lecture halls frequently leads to low motivation and student participation. Personalized gamification, a pedagogical approach that blends gamification and personalized learning, offers a solution to this challenge. This approach integrates gaming elements and personalized learning strategies to motivate and engage students while addressing their individual learning needs and differences. A comprehensive literature review analyzes 101 studies based on research design, intervention, outcome measures, and quality assessment. The findings suggest that personalized gamification can enhance student cognition in programming courses by boosting motivation, engagement, and learning outcomes. However, the effectiveness of personalized gamification depends on various factors, including the types of gaming elements used, the level of personalization, and learner characteristics. This article offers insights into designing and implementing effective personalized gamification interventions in programming courses. The findings may inform educators and researchers in programming education about the potential benefits of personalized gamification and its implications for educational practice.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2310"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Angle information assisting skeleton-based actions recognition.","authors":"Chengming Liu, Jiahao Guan, Haibo Pang, Lei Shi, Yidan Chen","doi":"10.7717/peerj-cs.2523","DOIUrl":"10.7717/peerj-cs.2523","url":null,"abstract":"<p><p>In human skeleton-based action recognition, graph convolutional networks (GCN) have shown significant success. However, existing state-of-the-art methods struggle with complex actions, such as figure skating, where performance is often unsatisfactory. This issue arises from two main factors: the lack of shift, scale, and rotation invariance in GCN, making them especially vulnerable to perspective distortions in 2D coordinates, and the high variability in displacement velocity, which depends more on the athlete's individual capabilities than the actions themselves, reducing the effectiveness of motion information. To address these challenges, we propose a novel cosine stream to enhance the robustness of spatial features and introduce a Keyframe Sampling algorithm for more effective temporal feature extraction, eliminating the need for motion information. Our methods do not require modifications to the backbone. Experiments on the FSD-10, FineGYM, and NTU RGB+D datasets demonstrate a 2.6% improvement in Top-1 accuracy on the FSD-10 figure skating dataset compared to current state-of-the-art methods. The code has been made available at: https://github.com/Jiahao-Guan/pyskl_cosine.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2523"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image based visual servoing with kinematic singularity avoidance for mobile manipulator.","authors":"Jesus Hernandez-Barragan, Carlos Villaseñor, Carlos Lopez-Franco, Nancy Arana-Daniel, Javier Gomez-Avila","doi":"10.7717/peerj-cs.2559","DOIUrl":"10.7717/peerj-cs.2559","url":null,"abstract":"<p><p>This article presents an implementation of visual servoing (VS) for a redundant mobile manipulator in an eye-in-hand configuration. We used the image based visual servoing (IBVS) scheme, which means the pose control of the robot is based on the error features in the image of a camera. Conventional eye-in-hand VS requires the inversion of a Jacobian matrix, which can become rank deficient, provoking kinematic singularities. In this work, the inversion of the Jacobian matrix is solved using damped least squares (DLS) to reduce singularities and smooth out discontinuities. In addition, a task prioritization scheme is proposed where a primary task performs the eye-in-hand IBVS task, and a secondary task maximizes a manipulability measure to avoid singularities. Finally, a gravity compensation term is also considered and defined on the basis of the image space error. The effectiveness of the proposed algorithm is demonstrated through both simulation and experimental results considering the Kuka YouBot.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2559"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the hidden depths: advancements in underwater image enhancement using deep learning and auto-encoders.","authors":"Jaisuraj Bantupalli, Amal John Kachapilly, Sanjukta Roy, Pavithra L K","doi":"10.7717/peerj-cs.2392","DOIUrl":"10.7717/peerj-cs.2392","url":null,"abstract":"<p><p>Underwater images hold immense value for various fields, including marine biology research, underwater infrastructure inspection, and exploration activities. However, capturing high-quality images underwater proves challenging due to light absorption and scattering leading to color distortion, blue green hues. Additionally, these phenomena decrease contrast and visibility, hindering the ability to extract valuable information. Existing image enhancement methods often struggle to achieve accurate color correction while preserving crucial image details. This article proposes a novel deep learning-based approach for underwater image enhancement that leverages the power of autoencoders. Specifically, a convolutional autoencoder is trained to learn a mapping from the distorted colors present in underwater images to their true, color-corrected counterparts. The proposed model is trained and tested using the Enhancing Underwater Visual Perception (EUVP) and Underwater Image Enhancement Benchmark (UIEB) datasets. The performance of the model is evaluated and compared with various traditional and deep learning based image enhancement techniques using the quality measures structural similarity index (SSIM), peak signal-to-noise ratio (PSNR) and mean squared error (MSE). This research aims to address the critical limitations of current techniques by offering a superior method for underwater image enhancement by improving color fidelity and better information extraction capabilities for various applications. Our proposed color correction model based on encoder decoder network achieves higher SSIM and PSNR values.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2392"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches.","authors":"Nadenlla RajamohanReddy, G Muneeswari","doi":"10.7717/peerj-cs.2496","DOIUrl":"10.7717/peerj-cs.2496","url":null,"abstract":"<p><strong>Background: </strong>A brain tumor is the development of abnormal brain cells, some of which may progress to cancer. Early identification of illnesses and development of treatment plans improve patients' quality of life and life expectancy. Brain tumors are most commonly detected by magnetic resonance imaging (MRI) scans. The range of tumor sizes, shapes, and locations in the brain makes the existing approaches inadequate for accurate classification. Furthermore, using the current model takes a lot of time and yields results that are not as accurate. The primary goal of the suggested approach is to categorize whether a brain tumor is present, identify its type and divide the affected area into segments.</p><p><strong>Methods: </strong>Therefore, this research introduced a novel efficient DL-based extension residual structure and adaptive channel attention mechanism (ERSACA-Net) to classify the brain tumor types as pituitary, glioma, meningioma and no tumor. Extracting features in brain tumor analysis helps in accurately characterizing tumor properties, which aids in precise diagnosis, treatment planning, and monitoring of disease progression. For this purpose, we utilized Enhanced Res2Net to extract the essential features. Using the Binary Chaotic Transient Search Optimization (BCTSO) Algorithm, the most pertinent features in terms of shape, texture, and colour are chosen to minimize complexity.</p><p><strong>Results: </strong>Finally, a novel LWIFCM_CSA approach is introduced, which is the ensemble of Local-information weighted intuitionistic Fuzzy C-means clustering algorithm (LWIFCM) and Chameleon Swarm Algorithm (CSA). Conditional Tabular Generative Adversarial Network (CTGAN) is used to tackle class imbalance problems. While differentiating from existing approaches, the proposed approach gains a greater solution. This stable improvement in accuracy highlights the suggested classifier's strong performance and raises the possibility of more precise and trustworthy brain tumor classification. In addition, our method's processing time, which averaged 0.11 s, was significantly faster than that of previous approaches.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2496"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel IoT-device management platform for on-the-fly generation of user interface <i>via</i> manifest-file addressing heterogeneity.","authors":"Nayancy Gupta, Gourinath Banda, Krishna Chaitanya Bommakanti, Venkata Srinivas Kothapalli","doi":"10.7717/peerj-cs.2480","DOIUrl":"10.7717/peerj-cs.2480","url":null,"abstract":"<p><p>The Internet of Things (IoT) is becoming indispensable across various application domains. In the domain of the consumer IoT, many original device manufacturers are coming up with a wide variety of IoT-based products and services catering with a range of applications such as personal-fitness training devices, healthcare devices, to smart-home things, <i>etc</i>. There is an accompanying smartphone application, called the IoT control app (ICA) through which such IoT devices are controlled. As of now, a user shall install a separate ICA app for each and every IoT device they own. This is because of the diverse heterogeneity inherent in the IoT domain. The installation of multiple ICAs leads to: memory congestion, steeper battery discharging and increased vulnerability-in smartphones. The diversity in IoT devices can be systematically abstracted away with text written in a manifest file. Based on this manifest file, a user-interface for the IoT-device gets generated on the fly by the ICA. In this article, we propose a manifest-based IoT-device platform including an application-layer protocol, which makes it possible for a single ICA App to control any compliant IoT-device after appropriate authentication. We developed a manifest-grammar for specifying error-free manifest files for different IoT-devices towards a seamless integration between ICA and IoT-devices.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2480"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing 5G network performance with dynamic resource allocation, robust encryption and Quality of Service (QoS) enhancement.","authors":"Abdullah M Alashjaee, Sumit Kushwaha, Hayam Alamro, Asma Abbas Hassan, Fuhid Alanazi, Abdullah Mohamed","doi":"10.7717/peerj-cs.2567","DOIUrl":"10.7717/peerj-cs.2567","url":null,"abstract":"<p><p>The International Telecommunication Union (ITU) predicts a substantial and swift increase in global mobile data traffic. The predictions suggest that this growth will vary from 390 EB (exabytes) to 5,016 EB (exabytes) from 2024 to 2030, accordingly. This work presents a new maximum capacity model (MCM) to improve the dynamic resource allocation, robust encryption, and Quality of Service (QoS) in 5G networks which helps to meet the growing need for high-bandwidth applications such as Voice over Internet Protocol (VoIP) and video streaming. Our proposed MCM model enhances data transmission by employing dynamic resource allocation, prioritised traffic management, and robust end-to-end encryption techniques, thereby guaranteeing efficient and safe data delivery. The encryption procedure is applied to the header cypher, while the output parameters of the payload are altered. This indicates that only the sender and recipient will possess exclusive knowledge of the final outcome. In result, the comparative analyses clearly show that the MCM model outperforms over conventional models in terms of QoS packet planner, QoS packet scheduler, standard packet selection, traffic management, maximum data rate, and bandwidth utilisation.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2567"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing ransomware defense: deep learning-based detection and family-wise classification of evolving threats.","authors":"Amjad Hussain, Ayesha Saadia, Musaed Alhussein, Ammara Gul, Khursheed Aurangzeb","doi":"10.7717/peerj-cs.2546","DOIUrl":"10.7717/peerj-cs.2546","url":null,"abstract":"<p><p>Ransomware is a type of malware that locks access to or encrypts its victim's files for a ransom to be paid to get back locked or encrypted data. With the invention of obfuscation techniques, it became difficult to detect its new variants. Identifying the exact malware category and family can help to prepare for possible attacks. Traditional machine learning-based approaches failed to detect and classify advanced obfuscated ransomware variants using existing pattern-matching and signature-based detection techniques. Deep learning-based approaches have proven helpful in both detection and classification by analyzing obfuscated ransomware deeply. Researchers have contributed mainly to detection and minimaly to family attribution. This research aims to address all these multi-class classification problems by leveraging the power of deep learning. We have proposed a novel group normalization-based bidirectional long short-term memory (GN-BiLSTM) method to detect and classify ransomware variants with high accuracy. To validate the technique, five other deep learning models are also trained on the CIC-MalMem-2022, an obfuscated malware dataset. The proposed approach outperformed with an accuracy of 99.99% in detection, 85.48% in category-wise classification, and 74.65% in the identification of ransomware families. To verify its effectiveness, models are also trained on 10,876 self-collected latest samples of 26 malware families and the proposed model has achieved 99.20% accuracy in detecting malware, 97.44% in classifying its category, and 96.23% in identifying its family. Our proposed approach has proven the best for detecting new variants of ransomware with high accuracy and can be implemented in real-world applications of ransomware detection.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2546"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11640932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing river and lake wastewater reuse recommendation in industrial and agricultural using AquaMeld techniques.","authors":"J Priskilla Angel Rani, C Yesubai Rubavathi","doi":"10.7717/peerj-cs.2488","DOIUrl":"10.7717/peerj-cs.2488","url":null,"abstract":"<p><p>AquaMeld, a novel method for reusing agricultural and industrial wastewater in rivers and lakes, is presented in this article. Water shortage and environmental sustainability are major problems, making wastewater treatment a responsibility. Customizing solutions for varied stakeholders and environmental conditions using standard methods is challenging. This study uses AquaMeld and Multi-Layer Perceptron with Recurrent Neural Network (MLP-RNN) algorithms to create a complete recommendation system. AquaMeld uses MLP-RNN to evaluate complicated wastewater, environmental, and pH data. AquaMeld analyses real-time data to recommend wastewater reuse systems. This design can adapt to changing scenarios and user demands, helping ideas grow. This technique does not assume data follows a distribution, which may reduce the model's predictive effectiveness. Instead, it forecasts aquatic quality using RNN-MLP. The main motivation is combining the two models into the MLP-RNN to improve prediction accuracy. RNN handles sequential data better, whereas MLP handles complex nonlinear relationships better. MLP-RNN projections are the most accurate. This shows how effectively the model handles complicated, time- and place-dependent water quality data. If other environmental data analysis projects have similar limits, MLP-RNN may work. AquaMeld has several benefits over traditional methods. The MLP-RNN architecture uses deep learning to assess complicated aquatic ecosystem interactions, enabling more proactive and accurate decision-making is the most accurate with a 98% success rate. AquaMeld is flexible and eco-friendly since it may be used for many agricultural and industrial operations. AquaMeld helps stakeholders make better, faster water resource management choices. Models and field studies in agricultural and industrial contexts examine AquaMeld's efficacy. This strategy enhances environmental sustainability, resource exploitation, and wastewater reuse over previous ones. According to the results, AquaMeld might transform wastewater treatment. River and lake-dependent companies and agriculture may now use water resource management methods that are less destructive.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2488"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142802486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kernel random forest with black hole optimization for heart diseases prediction using data fusion.","authors":"Ala Saleh Alluhaidan, Mashael Maashi, Noha Negm, Shoayee Dlaim Alotaibi, Ibrahim R Alzahrani, Ahmed S Salama","doi":"10.7717/peerj-cs.2364","DOIUrl":"10.7717/peerj-cs.2364","url":null,"abstract":"<p><p>In recent years, the Internet of Things has played a dominant role in various real-time problems and given solutions via sensor signals. Monitoring the patient health status of Internet of Medical Things (IoMT) facilitates communication between wearable sensor devices and patients through a wireless network. Heart illness is one of the reasons for the increasing death rate in the world. Diagnosing the disease is done by the fusion of multi-sensor device signals. Much research has been done in predicting the disease and treating it correctly. However, the issues are accuracy, consumption time, and inefficiency. To overcome these issues, this paper proposed an efficient algorithm for fusing the multi-sensor signals from wearable sensor devices, classifying the medical signal data and predicting heart disease using the hybrid technique of kernel random forest with the Black Hole Optimization algorithm (KRF-BHO). This KRF-BHO is used for sensor data fusion, while XG-Boost is used to classify echocardiogram images. Accuracy in the training phase with multi-sensor data fusion data set of proposed work KRF-BHO with XGBoost classifier is 94.12%; in the testing phase, the accuracy rate is 95.89%. Similarly, for the Cleveland Dataset, the proposed work KRF-BHO with XGBoost classifier is 95.78%; in the testing phase, the accuracy rate is 96.21%.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"10 ","pages":"e2364"},"PeriodicalIF":3.5,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}