Scalable Computing: Practice and Experience
type: channel
description: Topics of interest. The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE provides this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal focuses on algorithm development, implementation and execution on parallel and distributed architectures, as well on application of parallel and distributed computing to the solution of real-life problems. Electronic journal. SCPE provides open access to its content following the principle that making research freely available to the public supports a greater global exchange of knowledge. We invite you to have a look to the open content of the volumes and to consider to interact with this publication willing to promote your results and achievements. Publication or access fees are not requested.
publisher: Not nominated at this moment
language: en
publication name: Scalable Computing: Practice and Experience
issn: 1895-1767
items: Optimizing EfficientNetv2 Model with RandAugment Data Augmentation for Detecting Wheat Diseases in Smart Farming
items: The Application System of Intelligent Wearable Devices in Physical Education
items: Near Infrared Spectroscopy Detection of Soybean Fatty Acid Content based on Neural Network combined with Genetic Algorithm
items: Revolutionizing Cardiac Prediction based on Fog-Cloud-IoT Integrated Heart Disease Model
items: The Construction of Smart Cities and Big Data Governance Strategies Based on Artificial Intelligence
items: Analysis Of The Effect Of The Application Of Montage Artistic Expression Of Video Data Information Technology Processing In Film And Television Choreography
items: A Challenge-Response based Authentication Approach for Multimodal Biometric System using Deep Learning Techniques
items: Online Education Student Cognitive State Recognition Based on Improved Multi-task Convolutional Neural Network
items: Enhancing Scalable User Experience in Smart Home Systems with Ubiquitous Virtual Reality Interfaces
items: AFM AutoInt Intelligent Recommendation System based on Attention Mechanism and Automatic Interaction Modeling
items: Research on AI and Cognitive Decision-Making in Artistic Design Innovation and Application
items: Ensemble Transfer Learning for Automated Gauge Reading Detection and Prediction
items: Multi Path Gait Control Method for Bipedal Robots based on Deep Reinforcement Learning
items: Development of an Intelligent System for Enhanced Maintenance of Automobiles using Machine Learning
items: Enhanced Pre-processing Strategies for Accurate Diabetes Prediction in Healthcare using Noval Method: ANN+LDA
items: Healthcare-as-a-Service Provisioning using Cloud-of-Things: A Contemporary Review of Existing Frameworks based on Tools, Services and Diseases
items: Deep Analysis on the Color Language in Film and Television Animation Works via Semantic Segmentation Technique
items: Research on the Path of Empowering Rural Revitalization with E-commerce in Shaanxi Province under the Background of Digital Economy
items: Human Behavior Recognition in Complex Scenes Based on Deep Learning
items: Exploring Foreign Language Education using Personalized Learning Algorithms and Distributed Systems Based on Big Data Analytics
items: The Blockchain in the Design of Electronic Medical Record System Supporting Multimedia Communication Technology
items: A Novel IoT Framework for Identifying and Mitigating Security Threats
items: Software Defect Prediction Model based on AST and Deep Learning
items: Interactive Display System Integrating Information Technology in Environmental Art Design
items: Optimizing LSTM Hyperparameters with Whale Optimization Algorithm for Efficient Freight Distribution in Smart Cities
items: Intelligent Algorithms for College Physical Education Athlete Training Using Computer Big Data Technology
items: Distributed Systems Framework for Packaging Design Innovation using Visual Perception and Algorithm Optimization
items: Distributed Systems for Evaluating and Optimizing Environmental Art Design Using Image Processing
items: Network Traffic Anomaly Detection Algorithms on Distributed Systems using Cognitive Intelligence
items: Distributed Systems for Simulation Analysis of Motor Drive Systems using Adaptive Algorithms

Optimizing EfficientNetv2 Model with RandAugment Data Augmentation for Detecting Wheat Diseases in Smart Farming
type: item
description: Wheat diseases threaten global food security, necessitating improved detection methods. In this paper, we integrate EfficientNetv2 model and RandAugment data augmentation to accurately and efficiently identify wheat diseases. EfficientNetv2, known for its optimal mix of accuracy and computing efficiency, is reinforced by RandAugment, a versatile data augmentation approach that randomly modifies training data. This augmentation method greatly enhances the model’s generalisation and performance on new data. Our extensive experimentation reveals that this integrated technique improves model accuracy and robustness relative to baseline models. Proposed model gained the 96.73% accuracy on prescribed dataset. The results show that EfficientNetv2 and RandAugment can detect wheat illnesses on a large scale. This could change precision agriculture by enabling early and accurate disease management.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: Wheat Diseases,
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: , Detection,
  topic: keywords
subject: EfficientNetV2
  topic: keywords
subject: , Image Recognition,
  topic: keywords
subject: RandAugment
  topic: keywords
creator: Manisha Sharma
creator: Alka Verma
creator: Uma Rani
rights: Copyright (c) 2025 Manisha Sharma, Alka Verma, Uma Rani https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2087
ending page: 2104
doi: 10.12694/scpe.v26i5.4639

The Application System of Intelligent Wearable Devices in Physical Education
type: item
description: To address the challenge of real-time data collection and analysis in human motion data mining, the author proposes a system that integrates intelligent wearable devices into physical education. Initially, motion data is gathered through these smart devices, then transformed into binary format. This data undergoes cleaning and supplementation processes before being clustered. The Firefly Algorithm is employed to enhance the K-means clustering technique, which is then applied to the processed data. Experimental results indicate that this refined approach achieves an average recall rate of 97.12% and an average data mining accuracy of 98.42%, thus offering a valuable foundation for the real-time monitoring and assessment of students’ physiological metrics. This algorithm can be applied in student physical condition assessment, sports injury and fatigue monitoring, sports posture and movement assessment, personalized training and rehabilitation program development, scientific decision-making and management, and other aspects.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: Smart wearable devices
  topic: keywords
subject: Human movement
  topic: keywords
subject: Data mining
  topic: keywords
subject: Data cleaning
  topic: keywords
subject: K-means clustering algorithm
  topic: keywords
subject: data acquisition
  topic: keywords
creator: Yongliang Wang
rights: Copyright (c) 2025 Yongliang Wang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2027
ending page: 2035
doi: 10.12694/scpe.v26i5.4711

Near Infrared Spectroscopy Detection of Soybean Fatty Acid Content based on Neural Network combined with Genetic Algorithm
type: item
description: In order to solve the problem of poor performance in rapid analysis of soybean fatty acid content using traditional methods, the author proposes a near-infrared spectroscopy detection method for soybean fatty acid content based on neural network combined with genetic algorithm. The author first collected sample spectra and preprocessed them using mean centralization and Savitzky Golay smoothing differentiation method. Then, the optimal band was selected through segmented combination modeling, and genetic algorithm was used to further screen wavelength points sensitive to content prediction modeling. Finally, the spectral data was decomposed using principal component analysis (PCA), and the score matrix was input into a 3-layer neural network for training. The optimal model was established through parameter optimization. The experimental results show that the calibration relative analysis error RPD of the model based on genetic algorithm combined with neural network is, indicating that the model has good prediction accuracy and stability. To further validate the model’s reliability in practical applications, the correlation coefficient R between the predicted values and the standard values within the model’s prediction range was found to be 0.986, indicating a strong linear relationship between them. The relative deviation of each value is less than 2%, and the standard deviation of the difference is 0.0091. Paired t-test =1.2706 was performed on both methods, which is less than 2.354. This indicates that there is no significant difference between the analysis results of this method and the standard method, verifying the strong predictive ability of the model in practical applications. The model designed by the author can accurately and efficiently complete near-infrared spectroscopy detection.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: near-infrared spectroscopy
  topic: keywords
subject: Neural network
  topic: keywords
subject: Genetic algorithm
  topic: keywords
subject: Soybeans
  topic: keywords
subject: fatty acid
  topic: keywords
creator: Min Li
creator: Xiaocui Qi
creator: Rongyao Li
creator: Rufeng Wang
creator: Dandan Yu
rights: Copyright (c) 2025 Dandan Yu, Min Li, Xiaocui Qi, Rongyao Li, Rufeng Wang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2036
ending page: 2044
doi: 10.12694/scpe.v26i5.4714

Revolutionizing Cardiac Prediction based on Fog-Cloud-IoT Integrated Heart Disease Model
type: item
description: In a time when technology is having a profound effect on medical applications, the rapid remote diagnosis of any cardiac disease has proven to be a formidable obstacle. These days, computers can swiftly process a large volume of patient medical records. Recent developments in the IoT and medical applications, such as the IoMT, have opened the possibility of data diffusion among numerous locations pertaining to patients. This study presents the IHDPM, an integrated model for the prediction of cardiac disease that takes into account dimensionality declining through PCA (principal component analysis), feature collection over sequential feature selection (SFS), and classifications through the random forest (RF) classifier. The proposed model outperforms over different unadventurous classification methods, including LR (logistic regression), NB (naive Bayes), SVM (support vector machine), KNN (K-nearest neighbors), DT (decision trees), and RF, according to experiments conducted using the CHDD (Cleveland Heart Disease Dataset) as of the UCI-ML source and the Python programming linguistic. Medical professionals may find the proposed model useful for making accurate diagnoses of cardiac patients. While DL approaches may produce more accurate prediction results, it would be supplementary operative to reduce the extents count before cluster generation to improve the results.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: IoMT; IHDPM; SVM; KNN: RF; CHDD.
  topic: keywords
creator: Amit Kumar Chandanan
creator: Meena Rani
creator: Kiran Sree Pokkuluri
creator: Suman Singh
creator: Vaibhav Jaiswal
creator: Potu Narayana
creator: Vandana Roy
rights: Copyright (c) 2025 Dr Amit Kumar Chandanan, Meena Rani, Kiran Sree Pokkuluri, Suman Singh, Vaibhav Jaiswal, Potu Narayana, Vandana Roy https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2105
ending page: 2117
doi: 10.12694/scpe.v26i5.4715

The Construction of Smart Cities and Big Data Governance Strategies Based on Artificial Intelligence
type: item
description: To tackle the challenge of sluggish resource scheduling in smart city management, the author introduces a study focused on smart city development leveraging artificial intelligence and big data governance strategies. The research uses the Cloud Management Module to monitor the various hardware equipment, and establishes the target functionality of the Cloud Computing Resource Scheduling. Through the application of PSO to this goal function, a optimal scheme is created for the Cloud Resource Scheduling. Experimental results demonstrate that the system achieves optimal resource utilization, nearing 100%, whereas the other two systems have lower utilization rates, both under 90%. This indicates that, compared to similar systems, this approach offers superior overall utilization. The system is capable of efficiently managing smart cities and providing real-time monitoring of urban conditions. When scheduling resources, it achieves shorter task completion times and higher system efficiency, ensuring maximum resource utilization.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: Cloud computing
  topic: keywords
subject: smart city
  topic: keywords
subject: Management system
  topic: keywords
subject: Cultural particle swarm
  topic: keywords
subject: Resource scheduling
  topic: keywords
creator: Yinping Li
rights: Copyright (c) 2025 Yinping Li https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2045
ending page: 2052
doi: 10.12694/scpe.v26i5.4716

Analysis Of The Effect Of The Application Of Montage Artistic Expression Of Video Data Information Technology Processing In Film And Television Choreography
type: item
description: Film and television works integrate the changes of light and shadow in the camera, relying on the interplay of screen, character and artistic techniques to convey a new emotion and attitude to the viewer. As the means of artistic creation begins to shift toward perceptual and cognitive perspectives, the original artistic expressions are stretched to the limit in film and television choreography. As a ”big hit” in film and television choreography, the study of montage in the history of film choreography has never stopped: it is a sensual means of expression, but also an artistic technique that reunites light and shadow. This paper studies the connotation of montage artistic expression, discusses its application in film and television choreography, and combines video information processing technology to enhance its expressive power. The experimental results show that our method can effectively improve the application effect of montage in film and television editing and directing, and the introduction of video processing technology improves the editing and directing performance.
subject: Special Issue - Adaptive AI-ML Technique for 6G/ Emerging Wireless Networks
  topic: section
subject: Application practice
  topic: keywords
subject: video processing
  topic: keywords
creator: Xiaoxiao Li
creator: Yanxiang Zhang
rights: Copyright (c) 2025 YanXiang Zhang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 1943
ending page: 1954
doi: 10.12694/scpe.v26i5.4717

A Challenge-Response based Authentication Approach for Multimodal Biometric System using Deep Learning Techniques
type: item
description: Multimodal Biometric System (MBS) is an advanced progression of conventional biometric authentication system, which employ multiple biometric traits to enhance security. However, despite their advantages, these systems are vulnerable to presentation attacks, where adversaries use photos, replay videos or voice recordings to deceive the authentication process. Therefore, this paper proposes a challenge-response based approach using texture-based facial features and multidomain speech features. The challenge-response approach requires the user to utter a random word. Next, the system detects the user’s facial features (eye and mouth motion) and recognized speech text to confirm whether the authentication request originates from a legitimate user or an imposter. The feature-level fusion via concatenation method is used to combine these image-audio features, to reduce the overlap within the feature spaces and data dimensionality. The fused feature vector is then fed into the deep learning driven ensemble classifier CNN-BiLSTM to train and test the fused samples for user authentication. The performance evaluation is carried out using a self-built database with 55 users, achieving 96.81% accuracy, 98.20% precision and an Equal Error Rate (EER) of 3.37%. Moreover, the proposed approach surpasses different cutting-edge MBS, deep learning classifiers and image-audio fusion techniques on various performance metrics. Thus, the results underscore the effectiveness of the deep learning-based MBS in ensuring user authentication and spoof detection, demonstrating its considerable potential in bolstering the security of biometric systems against intricate presentation attacks.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: challenge-response
  topic: keywords
subject: deep learning
  topic: keywords
subject: multimodal biometric system
  topic: keywords
subject: authentication
  topic: keywords
subject: ensemble classifier
  topic: keywords
subject: pattern recognition
  topic: keywords
creator: Khushboo Jha
creator: Aruna Jain
creator: Sumit Srivastava
rights: Copyright (c) 2025 Khushboo Jha, Dr. Aruna Jain, Dr. Sumit Srivastava https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2118
ending page: 2129
doi: 10.12694/scpe.v26i5.4733

Online Education Student Cognitive State Recognition Based on Improved Multi-task Convolutional Neural Network
type: item
description: With the widespread application and development of internet technology in public education scenarios in China, the application of deep learning technology on online learning platforms is becoming increasingly widespread. This study aims to address the difficulty in determining students’ cognitive state under the current network learning mode, and proposes a face recognition algorithm for student cognitive state detection using multitask convolutional neural network image recognition technology. At the same time, research is conducted on extracting two-dimensional feature points of facial color images through cascading regression tree localization methods. In the practical application experiments of the method, the research method can effectively detect images with facial offset angles greater than 15° for students, and the cognitive state of online learning can be analyzed from the frequency detection of students’ blinking and yawning. From the results of image simulation experiments, it can be seen that this study proposes a cascaded regression tree localization optimization multitask convolutional neural network face recognition method, which has the highest image recognition accuracy of 86%, a recall rate of 0.85, and an f1 value of 0.855. The experimental results show that online learning state recognition based on image analysis can effectively monitor abnormal states during students’ learning process, improve students’ online learning efficiency, and provide necessary student state information support for teachers, promoting the improvement of online teaching quality.
subject: Special Issue - Transformative Horizons: The Role of AI and Computers in Shaping Future Trends of Education
  topic: section
subject: Multi-task convolutional neural network; Cascaded regression tree; Network teaching; Cognitive load; Teaching link
  topic: keywords
creator: Weijuan An
creator: Li Shen
creator: Yali Yuan
rights: Copyright (c) 2025 Weijuan An, Li Shen, Yali Yuan https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2231
ending page: 2243
doi: 10.12694/scpe.v26i5.4738

Enhancing Scalable User Experience in Smart Home Systems with Ubiquitous Virtual Reality Interfaces
type: item
description: The popularity of smart home systems has greatly increased over the past several years, which provide convenience, automation, and control over various areas of daily life at home. However, these technologies still have issues with scalable user experience, mainly because there aren’t any engaging and intuitive user interfaces. Incorporating pervasive virtual reality (VR) interfaces into smart home systems is the unique strategy proposed in this study to improve scalable user experience. The study’s goal is to use VR technology to develop engaging and simple user interfaces that seamlessly integrate with the actual surroundings of a smart home. Users can engage with their smart home appliances and services through a mixed-reality experience in which virtual items and information are seamlessly incorporated into their environment by donning portable VR headsets. This application considers various difficulties related to developing Ambient Assisted Living (AAL) solutions, including unique characteristics of each end user, appliance, technology, deployment, and data-sharing problems. The Smart Home Systems take advantage of Semantic Web technologies integration abilities and their capacity facing represent significant information into legal models. A virtual reality application called Smart Home Systems allows for residential settings’ setup and customization in AAL systems. Additionally, it effectively uses VR technology to streamline the creation of specialized AAL settings. The application and underlying framework were evaluated for each through two scenarios: designing a home setting specifically for specific scalable user categories.
subject: Special Issue - Unleashing the power of Edge AI for Scalable Image and Video Processing
  topic: section
subject: virtual reality (VR) interfaces
  topic: keywords
subject: Ambient Assisted Living (AAL)
  topic: keywords
subject: Smart Home Systems
  topic: keywords
subject: users
  topic: keywords
subject: customized design
  topic: keywords
subject: smart home appliances
  topic: keywords
creator: Natesh Mahadev
creator: Sowmya V L
creator: Shankar R
creator: Anitha Premkumar
creator: Syed Salim
creator: Rajesh Natarajan
rights: Copyright (c) 2025 Natesh Mahadev, Sowmya V L, Shankar R, Anitha Premkumar, Syed Salim, Rajesh Natarajan https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 1985
ending page: 2001
doi: 10.12694/scpe.v26i5.4748

AFM AutoInt Intelligent Recommendation System based on Attention Mechanism and Automatic Interaction Modeling
type: item
description: With the rapid growth of Internet data, intelligent recommendation systems are crucial for enhancing user experience and platform efficiency. Traditional algorithms struggle with high-dimensional sparse data and complex feature interactions. To address this, we propose the AFM-AutoInt model, integrating deep learning, attention mechanisms, and automatic feature interaction modeling. It utilizes embedding layers for dimensionality reduction, attention mechanisms for adaptive learning, and multi-layer self-attention for capturing high-order interactions. Experimental results show that AFM-AutoInt outperforms traditional methods in accuracy and robustness, making it a promising solution for next-generation recommendation systems.
subject: Special Issue - Adaptive AI-ML Technique for 6G/ Emerging Wireless Networks
  topic: section
subject: Intelligent recommendation system
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Attention mechanism; AFM AutoInt model
  topic: keywords
subject: Data sparsity
  topic: keywords
creator: Qiang Han
creator: Dong Liang
rights: Copyright (c) 2025 Dong Liang, Qiang Han https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 1955
ending page: 1963
doi: 10.12694/scpe.v26i5.4752

Research on AI and Cognitive Decision-Making in Artistic Design Innovation and Application
type: item
description: A revolutionary change in the creative environment is represented by the incorporation of Artificial Intelligence (AI) and cognitive decision-making in creative design. This study investigates how cognitive psychology and AI connect with creative design development and implementation. The study explores how AI might enhance human creativity by utilizing machine learning techniques, neural networks, and computational imagination to provide new opportunities in creative thinking. The study explores models of cognitive decision-making and looks at how AI systems might mimic and improve the cognitive processes that artists use to create their works. It draws attention to how AI can produce original design ideas, streamline design procedures, and offer individualized design alternatives. This project intends to investigate the consequences of AI-driven cognitive decision-making on the evolution of creative design through an integration of mathematical modeling and real-world experience, offering insights into the cooperative symbiosis among human creators and AI systems. The results point to an important change in creative endeavors wherein AI collaborates with artists to explore the frontiers of design innovation and creativity.
subject: Special Issue - Cognitive Computing for Distributed Data Processing and Decision-Making in Large-Scale Environments
  topic: section
subject: Artificial Intelligence, Cognitive Decision-Making, Artistic Design, Innovation, Application
  topic: keywords
creator: Huisan Wang
rights: Copyright (c) 2025 Huisan Wang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2244
ending page: 2252
doi: 10.12694/scpe.v26i5.4771

Ensemble Transfer Learning for Automated Gauge Reading Detection and Prediction
type: item
description: Pressure gauges and automatic reading methods for pointer gauges were the root causes of the problems that have motivated the development of an ensemble transfer learning strategy. The study suggests that to effectively generate and predict the present measurements of the gauge, it is necessary to use an ensemble learning method that incorporates transfer learning framework designs such as InceptionResnetV2 or DenseNet 201. The suggested methodology involves integrating the given data with ensemble model architectures and qualifying InceptionResnetV2 and Dense Net 201 models to forecast the present gauge value. The main focus of the proposed method is to develop a final angle with a clear reading, as well as improve the picture through techniques like enhancing its shape, size, and resolution. Image processing approaches help to achieve these objectives. The ensemble model achieved an accuracy of 98.34%, whereas the InceptionResNetV2 model experienced a loss of 8.34% in contrast stretching.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: Deep Learning
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Ensemble Model
  topic: keywords
creator: Hitesh Ninama
creator: Jagdish Raikwal
creator: Pushpa Raikwal
rights: Copyright (c) 2025 Hitesh Ninama, JAGDISH RAIKWAL, PUSHPA RAIKWAL https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2130
ending page: 2144
doi: 10.12694/scpe.v26i5.4786

Multi Path Gait Control Method for Bipedal Robots based on Deep Reinforcement Learning
type: item
description: We propose a multipath gait control strategy based on deep reinforcement learning (DRL) for bipedal robot motion planning on diverse and challenging terrains. Traditional control methods, such as PID controllers and model-based motion planning, often struggle in complex environments. These approaches typically underperform because they rely on precise mathematical models or predefined rules, making them ill-suited for nonlinear, uncertain, and dynamic settings. Conventional techniques also have difficulty adapting their control strategies in unpredictable and fluctuating terrains, where robots may encounter unforeseen disturbances, leading to instability or failure. Deep reinforcement learning is able to independently acquire optimal control methods from environmental feedback without requiring a precise model since it combines deep learning and reinforcement learning. In this work, we leverage deep reinforcement learning algorithms (DDPG, TRPO, PPO, A3C, SAC, etc.) based on actor-critic (AC) architectures to enable reliable gait control of bipedal robots in a continuous motion environment. The issue that traditional approaches have in challenging to converge complicated environments is solved by DRL, which, when compared to traditional methods, can effectively cope with the high nonlinearity of complex terrain and adaptively alter the strategy through continuous contact with the environment. Using goal-conditional techniques, we created a motion planning model and tested it on the actual hardware platform Cassie. According to the experimental results, the approach successfully transfers the simulation strategy to the actual environment, and the robot can accurately complete the goal task without global location feedback. It can also perform a variety of complex tasks, like jumping on discontinuous and flat terrain. Furthermore, the method exhibits significant robustness and adaptability through multithreaded asynchronous training and randomized strategy selection, …
subject: Special Issue - Adaptive AI-ML Technique for 6G/ Emerging Wireless Networks
  topic: section
subject: Reinforcement learning; Bipedal robot; Multi path gait control; Actor critic; Robot robustness
  topic: keywords
creator: Lehui Lin
creator: Pingli Lv
rights: Copyright (c) 2025 Pingli lyu https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 1964
ending page: 1973
doi: 10.12694/scpe.v26i5.4790

Development of an Intelligent System for Enhanced Maintenance of Automobiles using Machine Learning
type: item
description: In the ever-changing automobile industry, sustainable practices are of utmost importance. It is essential to perform preventative maintenance to accomplish sustainability objectives. Vehicles will have fewer unanticipated breakdowns and last longer as a result. This study introduces a machine learning model that has been optimized to enhance preventive maintenance operations in the automotive sector. To improve the efficacy of predictive maintenance in intelligent manufacturing systems, this paper presents an improved AdaBoost algorithm linked with big data analytics. First, in the proposed framework, massive datasets are gathered and preprocessed from sensors, IoT devices, and other sources in the industrial setting. Then, the meta-algorithm AdaBoost is used to improve the efficiency of subpar learners, allowing for reliable failure and deterioration prediction in machinery. Adjusting hyperparameters like the number of iterations and the learning rate is part of the algorithmic optimization process to strike a good balance between model accuracy and computational efficiency. The proposed model gains an accuracy level of 0.972 value, Precision level of 0.977 value, Recall level of 0.972 value and F1-score level of 0.974 value. By analyzing historical data, our algorithm can predict when problems will occur, enabling us to take quick action and minimize downtime. Improved maintenance scheduling and reduced environmental effects are outcomes of the proposed model’s use of cutting-edge optimization techniques, which boost the model’s predictive capabilities. The model achieves better results than the state-of-the-art methods in extensive trials conducted on a dataset from a leading automaker. It achieves significant improvements in maintenance efficiency and prediction accuracy. Sustainability in the automobile sector is a wider purpose of this study, which proposes a data-driven plan for maintenance that is strong and in line with economic and environmental aims.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: Optimized AdaBoost
  topic: keywords
subject: Predictive Maintenance
  topic: keywords
subject: Automobile Maintenance Optimization
  topic: keywords
creator: Neeraj Dahiya
creator: Edeh Michael Onyema
creator: Venkataramaiah Gude
creator: Neetu Faujdar
creator: Gaytri Devi
creator: Reenu Batra
rights: Copyright (c) 2025 Neeraj Dahiya, EDEH MICHAEL ONYEMA, VENKATARAMAIAH GUDE, NEETU FAUJDAR, GAYTRI DEVI, Reenu Batra https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2145
ending page: 2164
doi: 10.12694/scpe.v26i5.4792

Enhanced Pre-processing Strategies for Accurate Diabetes Prediction in Healthcare using Noval Method: ANN+LDA
type: item
description: In the recent past, so many chronic diseases have been emerging and spreading in the world and even in the developing and advanced countries as well. One of such serious chronic diseases is Diabetes Mellitus that covers and impacts the health of people from early age. Nevertheless, the available Machine Learning (ML) and Deep Learning (DL) approaches are unable to provide good predictions in patients relating to diabetes. In addition, this study evaluated the proposed pre-processing procedure on large datasets for diabetes prediction that contained outliner detection and removal, missing values imputation, and standardization, to improve diabetes ascertainment. This research evaluated the proposed pre-processing procedure on a large set of data by outlier identification and removal, missing values imputation and data standardization were done to improve diabetes forecast. To ensure rapid and accurate classification of diabetes, the researchers employed and initialized an Artificial Neural Network (ANN). Data was gathered from the PIMA Dataset and North California State University (NCSU). Following this, Bivariate filter was applied to sort out features which were relevant. The selected features were subsequently subjected to Pearson correlation towards feature set refinement considering a threshold below which features were eliminated and only the most effective features selected. From the results it was evident that the proposed approach was significantly better than the existing methods in terms of accuracy as it achieved a classification accuracy of around 93% as opposed to the other methods.
subject: Special Issue - Unleashing the power of Edge AI for Scalable Image and Video Processing
  topic: section
subject: Artificial Neural Network,
  topic: keywords
subject: Bivariate Filter
  topic: keywords
subject: Diabetes mellitus
  topic: keywords
subject: standardization
  topic: keywords
creator: Soumya K N
creator: Praveen N
rights: Copyright (c) 2025 Soumya K N, Rajapraveen.k.n nakka https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2002
ending page: 2015
doi: 10.12694/scpe.v26i5.4818

Healthcare-as-a-Service Provisioning using Cloud-of-Things: A Contemporary Review of Existing Frameworks based on Tools, Services and Diseases
type: item
description: With the advancements of the technologies, healthcare industry has become more digitized, data driven, patient-centric, innovative as well as collaborative. It has become possible to access and share patient information globally irrespective of time and locations. The traditional healthcare facilities have proved inadequate in facilitating better patient treatment and services as per the patient requirements. The modern healthcare provisioning involves the use of digital technologies and operations and manifest digitized healthcare facilities. The digital technologies such as cloud computing, Internet of Things and many other supports the facilitation of smart healthcare utilities and services. This paper describes how cloud computing and IoT deliver healthcare-as-a-service. Further, the various proposed healthcare frameworks integrated with healthcare field have been discussed such as based on technologies implemented, offered services, and focused disease(s). Also, this survey enlightens the comparisons of existing frameworks based on technologies used and based on the focused methodologies.
subject: Special Issue - Advancing Healthcare through Scalable Machine Learning: Overcoming Challenges and Embracing Innovations
  topic: section
subject: Cloud Storage
  topic: keywords
subject: Electronic Health Record
  topic: keywords
subject: Heart Disease
  topic: keywords
subject: mhealth
  topic: keywords
subject: Personal Health Record
  topic: keywords
subject: Robotics
  topic: keywords
subject: Technology-enhanced Healthcare
  topic: keywords
subject: Telehealth
  topic: keywords
creator: Shilpa
creator: Tarandeep Kaur
creator: Rachit Garg
creator: Deepak Prashar
creator: Sudan Jha
creator: Sultan Ahmad
creator: Jabeen Nazeer
rights: Copyright (c) 2025 Shilpa, Tarandeep Kaur, Rachit Garg, Deepak Prashar, Sudan Jha, Sultan Ahmad, Jabeen Nazeer https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2214
ending page: 2230
doi: 10.12694/scpe.v26i5.4823

Deep Analysis on the Color Language in Film and Television Animation Works via Semantic Segmentation Technique
type: item
description: Color functions as a distinct type of ideographic symbol in animation for film and television, playing a crucial role in enhancing visual narratives and conveying emotions. In different types of animation, such as fantasy, horror, or children’s genres, color language influences audience perception and can convey meanings beyond the capabilities of image language alone. For instance, bright colors may symbolize innocence in children’s animation, while darker shades may evoke tension or fear in horror. However, current approaches to representing color in animation often fail to capture its full semantic richness and ideographic potential. Existing methods primarily focus on image-based analysis, overlooking the deeper layers of meaning encoded in color language. In this paper, we address these gaps by utilizing semantic segmentation techniques to combine the three modalities of color, content, and text to establish a consistent representation of color language in animation. We propose a method for semantic segmentation of color-depth (RGB-D) images using two-stream weighted Gabor convolutional network fusion. A weighted Gabor orientation filter builds a deep convolutional network (DCN) capable of extracting feature information adaptive to changes in orientation and scale, allowing for orientation- and scale-invariant features. Dual-stream picture features - color and depth - are extracted using a broad residual-weighted Gabor convolutional network and then combined into a lightweight feature extraction network. To evaluate the ideographic functions of color language quantitatively, we conducted extensive experiments using open databases. Our proposed method outperforms existing RGB-D picture semantic segmentation algorithms, demonstrating its effectiveness in representing color language in animation.
subject: Special Issue - Adaptive AI-ML Technique for 6G/ Emerging Wireless Networks
  topic: section
subject: Semantic Segmentation
  topic: keywords
subject: Video Animation
  topic: keywords
subject: Color Language
  topic: keywords
creator: Yanxiang Zhang
rights: Copyright (c) 2025 YanXiang Zhang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 1974
ending page: 1984
doi: 10.12694/scpe.v26i5.4847

Research on the Path of Empowering Rural Revitalization with E-commerce in Shaanxi Province under the Background of Digital Economy
type: item
description: Utilising marketing data effectively can improve market mobility and competition, which will increase user numbers. In order to help businesses proactively respond to competition in the market of Shaanxi Province, this study validates and productizes a game theory-assisted machine learning scheme based on game decision making and conditional slack. The technology gives businesses a machine learning model to identify user carrying propensity and treat users differently. The technology can help businesses create targeted porting strategies and precisely identify "abnormal" users who are likely to port in or out, according to experimental results. The plan has also been effectively turned into a product, greatly enhancing the company's marketing effectiveness and, in the case of one company, raising the number of subscribers by about 50%. The seed set is analysed in a varied comparison between the movie consumption network and the Yahoo Answers network, and the interest preferences of movie-going customers are examined in several aspects.
subject: Special Issue - High-performance Computing Algorithms for Material Sciences
  topic: section
subject: user increase
  topic: keywords
subject: game theory
  topic: keywords
subject: machine learning
  topic: keywords
subject: marketing data
  topic: keywords
subject: comparative analysis
  topic: keywords
creator: Zhenya Zhao
creator: Xiaolan Feng
rights: Copyright (c) 2025 Xiaolan Feng https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2016
ending page: 2026
doi: 10.12694/scpe.v26i5.4851

Human Behavior Recognition in Complex Scenes Based on Deep Learning
type: item
description: In order to overcome some of the problems that have been encountered in the past, for example, due to their reliance on manual feature extraction and limitation of model generalization, this paper proposes a new method to identify people’s behavior in complicated situations. Based on Convolutional Neural Networks (CNN), this method has been proposed for the automatic extraction of a large number of datasets. In addition, the Long Short Term Memory (LSTM) network is used to capture the long-run dependence in time order. Lastly, we use the soft max classifier to classify the various actions of people. Experiments show that the CLT network is able to achieve a high performance of 97.5% over 13 different types of people, outperforming CNN alone, LSTM, and BP models on the DaLiAc dataset, demonstrating superior performance in human behavior recognition and classification. The accuracy, recall, and F1 score evaluation indicators of the CLT net model are the highest, while all indicators of the BP model are the lowest, indicating that the CLT net model has good stability and reliability in recognizing and classifying different human behaviors.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: Human behavior recognition
  topic: keywords
subject: Deep leaming
  topic: keywords
subject: Convolutional neural network
  topic: keywords
subject: Long Short Term Memory Network
  topic: keywords
creator: Ruifeng Hong
rights: Copyright (c) 2025 Ruifeng Hong https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2053
ending page: 2060
doi: 10.12694/scpe.v26i5.4857

Exploring Foreign Language Education using Personalized Learning Algorithms and Distributed Systems Based on Big Data Analytics
type: item
description: The limitations of traditional methods result in a lack of personalization and real-world immersion in language learning, particularly in foreign language learning skills. It leads to limited conversational practices and slower development. Also, traditional methods fail to adjust the respective needs of every individual learner and varying proficiency levels. To address these kinds of problems and to enhance foreign language education, the present study supports with its unique novel framework called the Cognitive Collaborative Language Optimizer (CCLO) model, which combines the benefits of collaborative filtering, fuzzy cognitive systems, and neighborhood-based recommendation algorithms based on distributed systems and big data analytics (BDA). By using real-time data based on every learner’s performance, preferences, and progress, the CCLO framework adjusts learning experiences for each individual and provides an effective path for language development. While neighborhood-based algorithms form clusters of learners with shared learning paths, on the other hand, collaborative filtering identifies patterns in learner behavior. It suggests education materials that work well for learners with comparable backgrounds. This guarantees a personalized learning experience. Managing uncertainty is an important function in CCLO, and this was performed using fuzzy cognitive systems, where learners commonly prove their partial knowledge of learning. CCLO provides the best experience by adjusting every learner’s needs and offers the benefit of vocabulary, grammar, and conversation practice at the correct time. Finally, the simulation of the suggested CCLO is conducted using a seven-week study based on the Chinese as a Foreign Language (CFL) dataset. The efficacy of the suggested model shows a notable improvement in conversation proficiency. The detailed illustrations and experiments are discussed.
subject: Special Issue - Cognitive Computing for Distributed Data Processing and Decision-Making in Large-Scale Environments
  topic: section
subject: Cognitive systems, collaborative filtering, foreign language learning, personalized learning, fuzzy logic, recommendation algorithms, immersive learning and AI based education.
  topic: keywords
creator: Jiajun Ma
rights: Copyright (c) 2025 Jiajun Ma https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2253
ending page: 2264
doi: 10.12694/scpe.v26i5.4879

The Blockchain in the Design of Electronic Medical Record System Supporting Multimedia Communication Technology
type: item
description: This paper proposes the design of electronic medical record system supported by multimedia communication based on blockchain technology. Blockchain technology ensures the secure storage and sharing of patient information through distributed ledger and smart contract algorithm. In this system, smart contracts are used to automatically execute cross-institutional data access control and audit functions to ensure the transparency and compliance of data access. At the same time, this paper introduces multimedia communication technology to support the efficient transmission and sharing of medical data, especially in diagnostic images, videos and voice. Simulation results show that the electronic medical record system based on blockchain has significantly improved data processing efficiency, security and reliability compared with traditional systems.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: Smart contract algorithm; Multimedia communication; Electronic medical record system; Blockchain.
  topic: keywords
creator: Xiong Jian
creator: Rajamohan Parthasarathy
creator: Yinqing Tang
creator: Binwen Huang
rights: Copyright (c) 2025 binwen huang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2061
ending page: 2069
doi: 10.12694/scpe.v26i5.4905

A Novel IoT Framework for Identifying and Mitigating Security Threats
type: item
description: The popularity of Internet of Things (IoT) devices has surged due to their applications in diverse areas such as e-Health, smart vehicles, and smart cities. However, the rapid deployment of these devices has led to an exponential increase in security attacks targeting IoT systems, making security a prime concern for the community. Securing IoT-based systems is challenging because the devices involved are often resource-constrained. Providing security to these systems requires a thorough understanding of their specific security needs, along with a systematic security engineering approach. Previous research lacks a systematic methodology for identifying and implementing security requirements. Therefore, there is a growing demand for a structured approach to identify security requirements, select appropriate algorithms, and ensure their effective implementation. While existing studies have extensively explored IoT security threats, they fall short of offering a structured method to comprehensively address these threats. This paper proposes a comprehensive security engineering framework that systematically identifies security threats by analyzing assets present over various layers of IoT system, considering their diverse roles. It includes creating repositories to identify potential vulnerabilities and applicable threats. Once threats are identified, they are evaluated for their severity level based on risk analysis. Following this, the framework focuses on designing the security solutions, where we proposed to add two new security services namely trust and data freshness besides the existing security services, algorithms are selected to mitigate threats by considering the domain and constraints of the devices involved. Ultimately, the security of the entire system is validated to ensure robustness. Throughout this process, we have developed comprehensive repositories for asset management, vulnerability-threat mapping, and algorithm-threat matching to help identify and …
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: Internet of Things
  topic: keywords
subject: security framework
  topic: keywords
subject: security issues
  topic: keywords
subject: security algorithms
  topic: keywords
subject: constraints
  topic: keywords
creator: Shruti Jaiswal
creator: Himani Bansal
creator: Shiv Naresh Shivhare
creator: Gulshan Shrivastava
rights: Copyright (c) 2025 Shruti Jaiswal, Himani Bansal, Shiv Naresh Shivhare, Gulshan Shrivastava https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2165
ending page: 2183
doi: 10.12694/scpe.v26i5.4909

Software Defect Prediction Model based on AST and Deep Learning
type: item
description: Software reliability prediction (SDP) theory is crucial for balancing software value and assessing efficiency. Traditional defect prediction relies on static code metrics for machine learning, but these handcrafted features fail to capture the code’s syntactic structure and semantic information. In order to further predict the defects of the software, the Abstract Syntax Tree (AST) of the program was parsed on the basis of the metric data, and extracted as feature vectors, and the data was encoded by dictionary mapping and word embedding as the input of Convolutional Neural Network (CNN). On this basis, the Long Short-Term Memory network (LSTM) and Multi-head attention mechanism were used to further optimize the network, and the particle swarm optimization (PSO) was used to select the hyperparameters of the model, and the defects were predicted by using the model. The results show that the model can learn syntax and semantic features well, and the estimation accuracy is higher and the bias is smaller.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: software defect prediction
  topic: keywords
subject: Abstract Syntax Tree
  topic: keywords
subject: Convolutional Neural Network
  topic: keywords
subject: Long Short-Term Memory
  topic: keywords
subject: Multi- Head Attention
  topic: keywords
creator: Zezhi Ye
creator: Chenghai Yu
creator: Zhilong Lu
rights: Copyright (c) 2025 Zezhi Ye, Chenghai Yu, Zhilong Lu https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2183
ending page: 2195
doi: 10.12694/scpe.v26i5.4910

Interactive Display System Integrating Information Technology in Environmental Art Design
type: item
description: In order to solve the problems of dimensionality reduction and color display of spectral data after redundancy reduction in interactive display in environmental art design, the author proposes a research on an interactive display system that integrates information technology in environmental art design. The author first performs principal component transformation on the spectral data cube, assigning the first three components to the black and white channels, red and green channels, and yellow and blue channels in the color space. Then, after spatial transformation to sRGB space, the data is segmented and translated to the range of 0-1, mapped to 8-bit RGB, and single item evaluations of standard deviation, entropy, and average gradient are performed on each translated image. After all translations are completed, a comprehensive evaluation is performed on all evaluation values, and the interval with the highest comprehensive evaluation value is selected to output the mapping. The experimental results show that although this algorithm has a slightly longer time overhead, it is still far less than the acquisition time of 6.63 seconds for one spectral data cube, and is suitable for real-time detection applications of hyperspectral data. The fusion of images can maximize the energy, information, and clarity of the images, which is beneficial for the rapid recognition and judgment of the human eye.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: Spectroscopy
  topic: keywords
subject: Fusion display
  topic: keywords
subject: Evaluation and selection
  topic: keywords
subject: Environmental art design
  topic: keywords
subject: Interactive Display
  topic: keywords
creator: Yuanyuan Yao
creator: Zhaochen Wang
rights: Copyright (c) 2025 Yuanyuan Yao, Zhaochen Wang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2070
ending page: 2077
doi: 10.12694/scpe.v26i5.4911

Optimizing LSTM Hyperparameters with Whale Optimization Algorithm for Efficient Freight Distribution in Smart Cities
type: item
description: Smart cities save logistics and operational expenses by optimizing freight distribution. This paper presents LSTM hyperparameter adjustment to optimise freight allocation using the Whale Optimization Algorithm (WOA). Traditional hyperparameter tuning struggles with freight logistics’ complexity and dynamism. WOA, a revolutionary bio-inspired optimization approach, finds optimal LSTM network hyperparameters. Our integrated solution fine-tunes LSTM hyperparameters using WOA to increase forecast accuracy and efficiency. The solution is tested on many smart city freight distribution scenarios. To prove the method works, prediction accuracy, computing efficiency, and convergence rate are measured. To determine how well the model detects data patterns and variations, the authors compare anticipated and real traffic flows using MAE, MSE, RMSE, etc. The proposed model’s root mean squared error is 0.23912122600654664 and achieved MAE value of 0.17255859883764077. The WOA-optimized LSTM model outperforms hyperparameter tuning in prediction accuracy and convergence speed. This optimises resource allocation and reduces environmental effect in freight distribution, enabling smart city concepts. These findings affect urban logistics and encourage more investigation.
subject: Special Issue - Recent Advancements in Machine Intelligence and Smart Systems
  topic: section
subject: LSTM model
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: smart cities
  topic: keywords
subject: Freight distribution
  topic: keywords
subject: Whale optimization model
  topic: keywords
creator: Yogesh Kumar Sharma
creator: Bakeeru Mery Sowjanya
creator: Mylapalli Kanthi Rekha
creator: Iyyappan Moorthi
creator: Ajay Kumar
creator: Susheela Hooda
rights: Copyright (c) 2025 YOGESH KUMAR SHARMA, BAKEERU MERY SOWJANYA, MYLAPALLI KANTHI REKHA, IYYAPPAN MOORTHI, Ajay Kumar, Susheela Hooda https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2196
ending page: 2213
doi: 10.12694/scpe.v26i5.4945

Intelligent Algorithms for College Physical Education Athlete Training Using Computer Big Data Technology
type: item
description: In order to solve the problems of long training content mastery time, poor training effect, and high cost in traditional training systems, the author proposes the use of computer big data technology in the research of intelligent algorithms for athlete training in university physical education. The author begins by gathering sports data from athletes, then utilizes a virtual reality perception interaction model to feed the data into the virtual environment generation unit. Data fusion is performed under the supervision of the simulation management module. Finally, combined with the motion behavior interpretation in the database and image card of the sports simulation training data unit, simulation 3D modeling is carried out in the 3D model processor according to user settings. The experimental results demonstrated that athletes trained with this system (the third group) had a markedly better understanding of the training content compared to those trained with the other two systems (the first and second groups) after the first week, achieving a mastery rate of 73.875%. As the duration of the training increased, all groups showed improved mastery of the content. This system enhances athletes’ training effectiveness while also reducing training costs, offering significant practical application value.
subject: Speciai Issue - Deep Learning in Healthcare
  topic: section
subject: Virtual Reality Technology
  topic: keywords
subject: Athletes
  topic: keywords
subject: Simulation training
  topic: keywords
subject: Sports data
  topic: keywords
creator: Hanyang Cui
creator: Xinyu Yang
rights: Copyright (c) 2025 Hanyang Cui, Xinyu Yang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2078
ending page: 2086
doi: 10.12694/scpe.v26i5.4949

Distributed Systems Framework for Packaging Design Innovation using Visual Perception and Algorithm Optimization
type: item
description: Food consumed by humans is becoming more and more customized to fit each person’s unique demands, with a vast array of product labels readily available. As a result, many companies are beginning to concentrate on enhancing the practicality of contemporary packaging. Throughout the lifetime of a user-product engagement, sensory paradigms and emotional responses may shift. Traditional product packaging layout is largely based on the designer’s emotional imagination and prior events; however, it is limited by uncontrollable content and a lack of expert advice; most earlier studies involving mental analysis of images focused on predicting the most prevalent viewers feelings. There are situations when an image’s general impression is insufficient for practical purposes since the emotions it arouses are very subjective and differ from viewer to viewer. The proposed methodology uses Genetic Algorithm based Multi-Layer Ant Colony Optimization for analysing visual perception and emotion perception to identify the senses of human being. A significant set of images called Image-Emotion-Social-Net is utilized to assess categorized and multivariate attitude representations. The collection, which comes from Flickr, has more than a million photos uploaded by more than 9,000 members. The results of this dataset’s research indicate that the suggested approach performs better in personalized emotional identification than several contemporary methods. In comparison to other current methods, the experimental findings demonstrate that the suggested approach obtains a high packaging layout excellence rate of 95.1%, a performance success rate of 98.5%, and a mean square error rate of 1.5%.
subject: Special Issue - Cognitive Computing for Distributed Data Processing and Decision-Making in Large-Scale Environments
  topic: section
subject: Distributed Systems Framework, Packaging Design, Visual Perception, Algorithm Optimization
  topic: keywords
creator: Ying Huang
rights: Copyright (c) 2025 Ying Huang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2265
ending page: 2274
doi: 10.12694/scpe.v26i5.5022

Distributed Systems for Evaluating and Optimizing Environmental Art Design Using Image Processing
type: item
description: Improving the visual appeal and practicality of areas is a major function of environmental art design. Nevertheless, conventional techniques for assessing and improving such designs are frequently arbitrary and ineffective. In order to assess and improve environmental art design, this study suggests a distributed system that makes use of Generative Adversarial Networks (GANs) and sophisticated image processing methods based on the characteristics of Computer-aided design (CAD). The methodology is based on distributed demand-side management in intelligent energy systems and emphasizes the decentralization of computational resources for increased flexibility and effectiveness. In order to model different design situations and improve based on aesthetic characteristics like color balancing, spatial arrangement, and visual balance, the system uses GANs for creating images and design transmission. This method’s implementation in a distributed framework speeds up the assessment procedure and allows for continuous improvement and real-time feedback. A comparative examination of the findings from the experiment highlights the remarkable quality and effectiveness of the strategy presented in this study, which performs better than alternative strategies when it comes to of precision, recall, and F1 score. The results validate the superior performance of the suggested approach in relation to component extraction and recognition in CAD environmental artwork design. It is expected that this will support strong evidence for real-world applications and further research advancements in relevant sectors.
subject: Special Issue - Cognitive Computing for Distributed Data Processing and Decision-Making in Large-Scale Environments
  topic: section
subject: Distributed Systems, Optimization, Environmental Art Design, Image Processing, Deep Learning
  topic: keywords
creator: Hui Wang
rights: Copyright (c) 2025 Hui Wang https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2275
ending page: 2285
doi: 10.12694/scpe.v26i5.5025

Network Traffic Anomaly Detection Algorithms on Distributed Systems using Cognitive Intelligence
type: item
description: Network traffic monitoring is one of the important roles to maintain the security and confidentiality between distributed systems particularly in detecting early cyber threats. Distributed system is a large network interconnected device, which are connected with one another. So, detecting anomalies is a major challenge in these systems. Traditional systems fail to detect anomalies in early stages, because threats are too advanced which are not handled by the traditional capacities. To address this issue the present study proposed and improved version network traffic monitoring system called (Net-IV). This approach combines the advantages of 1D-CNN, Long Term-Short Term Memory (LSTM) and GRU (Gated Recurrent Units). According to this, 1D-CNN which is well known for its feature extraction ability, whereas LSTM helps to analyse the temporal dependencies, finally GRU refine the overall performance and helps to detect anomalies with greater precision. The model was evaluated using CIC-1DS-2017 dataset, a complete benchmark dataset for intrusion detection system. Through the simulation, we observed that the suggested Net-IV achieves a remarkable accuracy rate of 99.78% and F1-Score of 99.56% which is 0.05% higher than the existing DCGCANet model. Thus, the results suggested that the proposed Net-IV system could be effectively installed in real-time, to protect the distributed system confidentialities from various forms of cyber-attacks.
subject: Special Issue - Cognitive Computing for Distributed Data Processing and Decision-Making in Large-Scale Environments
  topic: section
subject: Network traffic anomaly detection, CNN, LSTM, GRU, distributed system, cognitive intelligence
  topic: keywords
creator: Ning Pan
rights: Copyright (c) 2025 Ning Pan https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2286
ending page: 2295
doi: 10.12694/scpe.v26i5.5037

Distributed Systems for Simulation Analysis of Motor Drive Systems using Adaptive Algorithms
type: item
description: High processing needs and latency make it difficult to simulate motor drive systems in distributed environments, which affect accuracy and real-time performance. Optimizing motor control and cutting energy use require effective modeling. Conventional simulation techniques have trouble scaling up and down, frequently demanding large amounts of resources and being unable to adjust to changing load circumstances, which leads to sluggish or imprecise simulations. This method improves scalability and lowers latency by dynamically adjusting computing loads in a distributed system through the use of adaptive algorithms. It improves the accuracy and efficiency of simulation by utilizing real-time adaption and parallel processing. The purpose of this work was to suggest distributed systems for motor drive system simulation analysis utilizing adaptive algorithms. Initially, the dataset was gathered from a test bench-mounted momentum permanent magnet synchronous motor (PMSM) in a three-phase system motor vehicle. The exponentially weighted moving standard deviation (EWMS) utilized in standardized data process representations for training. We proposed the Adaptive Controller with dynamic fuzzy system ensemble (AC-DMFSE) for distributed systems for simulation analysis of motor drive systems. To optimize motor performance in dynamic situations, adaptive techniques are used, such as fuzzy logic-based optimization and model predictive control. Our test findings show that the suggested distributed technique reduced simulation times and MSE while enhancing the accuracy of system performance evaluation. The foundation for scalable and effective motor drive system simulations is laid by this work, which also offers insightful information for improving the systems’ performance in practical applications.
subject: Special Issue - Cognitive Computing for Distributed Data Processing and Decision-Making in Large-Scale Environments
  topic: section
subject:
  topic: keywords
  value: Simulation Analysis, Motor Drive Systems, permanent magnet synchronous motor (PMSM), exponentially weighted moving standard deviation (EWMS), Adaptive Controller with dynamic fuzzy system ensemble (AC-DMFSE)
creator: Lili Kong
creator: Chunqiu Yi
rights: Copyright (c) 2025 Lili Kong, Chunqiu Yi https://creativecommons.org/licenses/by/4.0
date: 2025-07-14
publication date: 2025-07-14
starting page: 2296
ending page: 2305
doi: 10.12694/scpe.v26i5.5039