Journal of Artificial Intelligence and Technology
The Journal of Artificial Intelligence and Technology is a Gold Open Access and peer-reviewed journal that publishes articles on the theoretical, technological, and experimental aspects of artificial intelligence and related studies. The journal aims to be a high-level international academic journal that provides a prestigious platform for researchers and practitioners in the focused artificial intelligence community. Frequency: Continuous Editor-in-Chief: Yinong Chen, Arizona State University, USA CiteScore 2024: 11.0 | SJR 2024: 0.712 Submission to first decision: 59 days Acceptance to publication: 12 days Acceptance rate: 25% ISSN: 2766-8649 © 2025 Intelligence Science and Technology Press Inc.
Intelligence Science and Technology Press
A Survey on Evolutionary Multitask Optimization
Traditional evolutionary algorithms (EAs) typically search from scratch. In the modern era of artificial intelligence(AI), solving multiple, related problems simultaneously has become a critical challenge. In practical applications, people often need to handle and complete multiple tasks simultaneously. Based on this demand, evolutionary multitask optimization (EMTO) has emerged as a powerful paradigm within the broader field of computational intelligence. EMTO is considered an effective method to provide optimal solutions for each specific task by promoting knowledge transfer between different optimization tasks, mirroring concepts like transfer learning and multitask learning in mainstream AI. Due to the powerful ability of EMTO in parallel search, it has attracted significant attention from researchers in the field of evolutionary computing, thus promoting extensive research on the application of EMTO. This paper comprehensively reviews the progress of EMTO research in recent years using a manual and systematic literature review methodology. First, we provide an in-depth mathematical description of the evolutionary multitask optimization problem and introduce the core framework of the multi-factorial EA. Subsequently, the basic implementation methods of EMTO and its various extensions are classified and summarized. Finally, we discuss in detail the theoretical analysis, benchmark problems, and practical applications of EMTO. At the end of this paper, we summarize the content and propose possible directions for future research.
Copyright (c) 2025 Authors https://creativecommons.org/licenses/by/4.0/
Motion Capture Algorithm for Students’ Physical Activity Recognition in Physical Education Curriculum
Physical training learning is one of the important ways to raise the national physical quality and health level. However, there are many problems in traditional physical education, such as the difficulty to identifying the effectiveness of physical education curriculum and low level of repetitive exercise content. In order to solve this problem and improve the curriculum quality of current middle school physical education courses, a motion capture algorithm (MCA) based on convolutional neural network and long-term memory network is proposed, and a student physical activity capture model is constructed based on the fusion algorithm. In the performance comparison test of the fusion algorithm proposed in this study, the loss value and accuracy of this fusion algorithm are 0.045 and 0.921, respectively, significantly superior to the comparison algorithm. Then in the empirical analysis, the accuracy rate of this MCA model proposed in this study for students’ walking posture recognition in physical education courses is 91.5%, which is better than the comparative capture method. This MCA can accurately capture the physical activities of students in physical education courses, which has practical application significance.
Copyright (c) 2023 Authors https://creativecommons.org/licenses/by/4.0/
Privacy Protection Based on Federated Learning
With the development of artificial intelligence technology, more and more fields will collect relevant user data and provide users with a better experience through data analysis. But there are also risks involved in the process of data collection, namely how to protect personal privacy data. To address this issue, this study combined differential privacy, convolutional neural networks (CNNs), and federated averaging algorithms to construct a privacy protection model. The study first utilized the federated average algorithm to handle data imbalance, ensuring that each analyzed data is in a balanced state. Then, based on of data balancing, a new algorithm model was constructed using differential privacy and CNNs. Finally, it utilized a number of public datasets to verify the role of the model in privacy protection. The results showed that the model can achieve recognition accuracy of 97.27% and 93.15%, respectively, for data under the influence of privacy budget and relaxation factor. Meanwhile, the classification accuracy of the model for data can reach 95.31%, with a regression error of 9.03%. When the local iteration number of the device was 30, the testing accuracy can reach 95.28%. This indicates that methods on the grounds of federated averaging algorithm and differential privacy can maintain the accuracy of the model while protecting user privacy. The application research of models has strong practical significance.
Copyright (c) 2024 Authors https://creativecommons.org/licenses/by/4.0/
Large Language Models as Computational Linguistics Tools: A Comparative Analysis of ChatGPT and Google Machine Translations
This study investigates the effectiveness of large language models (LLMs), specifically ChatGPT, in machine translation (MT) and compares them with traditional tools like Google Translate. The research focused on translating speeches by King Abdullah II of Jordan, delivered in Arabic and English at significant international events in 2023. The study evaluated the translations based on meaning, functional and textual adequacy, target language mechanics, style, register, and idiomaticity. The analysis revealed that Google Translate’s Arabic–English translations were deficient, with contextual accuracy and meaning issues necessitating major revisions. The English–Arabic translations by Google Translate also required significant edits due to literal translation practices and inadequacies in several areas. Contrariwise, ChatGPT’s Arabic–English translations were rated as acceptable, needing only minor edits, and offered more natural-sounding translations. The English–Arabic translations by ChatGPT, while better than Google Translate, still showed some deficiencies but were deemed acceptable with minor adjustments. The study underscores the irr-eplaceability of human translators in ensuring accurate and contextually rich translations. However, the study also highlights the potential of LLMs, such as ChatGPT, to significantly enhance the translation process. Developers are encouraged to enhance LLMs’ contextual understanding and natural language processing capabilities. This involves expanding training datasets to include diverse and context-rich examples and improving the models’ ability to handle different registers, styles, and idiomatic expressions. The study strongly advocates for a collaborative model in the translation industry that integrates machine translation with human expertise to enhance efficiency while maintaining quality. These insights are crucial for driving advancements in developing and applying machine translation tools, ensuring they complement rather than replace human translators.
Copyright (c) 2024 Author https://creativecommons.org/licenses/by/4.0/
Mortality Trends and Its Impact on the Pension Liabilities
Malaysia is forecasted to become an aging nation by 2030. As this may reflect an improvement in the healthcare system, a lot of parties need to adapt to these changes immediately including the government, which is responsible for the wellbeing of the public sector employees by providing a defined-benefit scheme during their retirement. The study aims to perform big data processing and analyze Malaysian mortality trends by expanding the Malaysian Abridged Life Table for the past 20 years using the six-point Lagrangian interpolation method, which is a key machine learning algorithm used today. Apart from that, this study also aims to examine the impact of mortality changes on pension liabilities by using actuarial valuation methods, which is projected unit credit (PUC) method. The sensitivity analysis will be conducted to observe the impact of the changes under five scenarios with four different ages at the time of valuation. Based on the empirical results, it was found that the six-point Lagrangian interpolation method shows a satisfactory outcome as the error recorded is below 1 percent. The result shows that there is a decreasing pattern in the mortality rate for the past 20 years, which will eventually result in population aging in Malaysia. Furthermore, the impact of reduced mortality rate on pension liabilities was also found to be significant. However, the impact can be mitigated by increasing the retirement age as life expectancy of the Malaysian population is believed to continue to increase due to aging population.
Deep Learning-Based Prediction Model for Crude Palm Oil Prices Using News Sentiment Analysis with Sliding Window
Crude palm oil (CPO) price prediction plays an important role in agricultural economic development. Various economics and agricultural-related factors have been used to predict CPO prices. Nevertheless, understanding news sentiment features will also be important in CPO price prediction. This paper proposes a CPO price prediction model to help the plantation organizations in the palm oil sector to successfully anticipate CPO price fluctuations and manage the resources more effectively. The CPO price behavior is nonlinear in nature, and thus prediction is very difficult. In this paper, an improved version of recurrent network, long short-term memory (LSTM)-based CPO price prediction model with news sentiment, is used to produce an enhanced prediction model. The findings of this study show that the LSTM-based forecasting model with news headline sentiment using a six-month sliding window produced the best result in forecasting the CPO price movement compared to other sliding window sizes.
Availability Evaluation of Cross Project Software Based on GAKT and Supervised Learning in Personalized Innovation Education for College Students
The availability evaluation of cross project software is crucial for personalized innovation education and learning of college students. Ensuring the stability and efficiency of educational resources and tools is the key to improving learning outcomes. This study proposes an evaluation model that integrates MeanShift clustering method and K-nearest neighbor (KNN) algorithm. The approach enhanced the feature selection of the model and improved the model by combining attention mechanism. The experimental results showed that the proposed model performed well in multiple aspects. In addition to exhibiting superior convergence performance, the proposed model also demonstrated a significant advantage over naive Bayesian and gradient-enhanced decision tree models in terms of delay performance. The optimal delay was 2.47 s, and the data volume was as low as 14.12 MB. Moreover, its accuracy reached 94.11%, which was 6.61% and 19.11% higher than naive Bayesian and gradient-enhanced decision trees, respectively. This model was of practical significance for optimizing the software tools for personalized and innovative education and learning of college students. The MeanShift-KNN model can provide educational institutions with a more reliable and efficient evaluation tool. The proposed model can better adapt to and meet the personalized learning needs of students, while also reducing potential learning interruptions caused by software unavailability. In addition, the efficient data processing and accuracy performance of the model mean that high-quality availability assessments can also be achieved in resource-constrained environments.
Charging Pile Fault Prediction Model Based on GRU Network and WOA
The global energy structure is transforming, and new energy vehicles (NEVs) are becoming the future of the automobile industry. However, the development of charging piles (CPs) and related facilities has not kept pace with the growth of NEVs. This study uses the gated recurrent unit (GRU) network and the whale algorithm to construct a high-performance CP fault prediction model. The proposed model, which utilizes the whale algorithm to prevent the GRU network from falling into local optima, demonstrates improved predictive information extraction and prediction ability. The experimentally verified results indicate that the proposed model achieved 92.02% prediction accuracy, 85.66% recall, and 93.87% F1 value. Additionally, the proposed model demonstrates excellent computational ability with an average running time of under 5 minutes on both datasets. This result is a substantial reduction from the control model’s running time. The experimental findings show that the study’s suggested model has a good ability to anticipate fault data. Its sophistication is verified by comparative tests, which can provide a reference for subsequent research.
Conductivity Prediction Method of Solid Electrolyte Materials Based on Pearson Coefficient Method and Ensemble Learning
In order to screen solid electrolytic materials with high ionic conductivity more quickly and accurately, a conductivity prediction method for solid electrolyte materials based on logistic regression model and random forest regression model is constructed. The method first selects 20 characteristic descriptors related to ionic conductivity from material library according to four dimensions: structural stability, metal stability, electronic conductivity and oxidative decomposition stability. The enumeration method and feature selection method are used to combine different features. Then, the dimension reduction of the feature combinations is carried out by Pearson coefficient method, so as to screen the optimal feature combinations. Finally, according to the optimal feature combination and data subset, the constructed random forest ensemble learning model is utilized to predict the ionic conductivity of other solid-state electrolyte materials in the material library, in order to find the solid-state electrolyte materials that can meet the high conductivity. The results indicate that as the number of features in the combination increases, the prediction performance of the model shows a trend of first increasing and then decreasing. When the feature descriptor in the combination is 7, the maximum AUC (Accuracy) value is reached, and the optimal feature in this case contains 7 optimal feature subsets corresponding to feature descriptors, namely the standard deviation of the average adjacency number of Li atoms, the standard deviation of Li-X ion bonds, the average electronegativity of the straight path, the average width of the straight path, the average atomic volume, the average Li-Li bond, and the filling fraction of the sublattice. On the basis of the optimal feature screening, the average prediction accuracy of the logistic regression model is 87.13%, which has a high accuracy. Using the random forest regression model for prediction, the average absolute error and root mean square error obtained are only 0.237 and 0.134, and compared with classical classification methods such as KNN, SVM and Adaboost, they are smaller, which can better predict the ionic conductivity of solid electrolyte materials. This proves that the constructed method can quickly and accurately produce high ionic conductivity materials in solid electrolyte materials, which is worthy of further research and promotion.
Local Wisdom in Fiction Text Comprehension Between Students and AIs
This study aims to compare the level of understanding of local wisdom values in short stories between students and artificial intelligence (AI) systems. The participants were 474 students from various universities in Indonesia: Al-Asyariah Mandar University, Makassar State University, Manado State University, West Sulawesi University, and Timor University. Meanwhile, five AIs were involved as nonhuman subjects: ChatGPT, Chatsonic, Claude AI, Gemini, and Perplexity. The analysis techniques used in this study include independent sample t-test and Fisher’s one-way ANOVA to compare the understanding of local wisdom between students and AI systems as well as between different groups of students. Findings show that AIs have a significantly higher understanding of local wisdom than university students, with AIs such as ChatGPT and Claude AI significantly outperforming. In addition, females have a significantly better understanding of local wisdom than males, while there are no significant differences based on domicile. Students from Al-Asyariah Mandar University have a better understanding. The implications of this study indicate the need for more inclusive and gender-sensitive curriculum development and increased integration of AI technology in education to enrich the understanding of local wisdom. Future research can explore other factors influencing the understanding of local wisdom and evaluate the long-term impact of AI in culture and literature learning.
A Privacy-Preserving System for Alzheimer’s Disease Detection Based on Federated Learnings
Alzheimer’s disease (AD) is a severe neurodegenerative disorder that primarily affects the elderly. Early detection is crucial for enabling timely interventions and slowing the disease’s progression. A promising approach for early detection involves analyzing audio data collected from elderly individuals in their homes using Internet of Things (IoT) devices. However, this method presents significant challenges concerning privacy and data security. This paper introduces Efficient Differential Privacy-Alzheimer’s Detection (EDP-AD), an efficient and privacy-preserving system. The system utilizes small IoT devices to collect audio data from elderly individuals. By integrating machine learning, federated learning (FL), and differential privacy (DP) techniques, EDP-AD ensures that raw audio data remains local while breaking down data silos, thereby preventing potential privacy leaks during the disease detection process. To enhance system efficiency, a sparse mask updating algorithm based on Top-k is proposed. This algorithm reduces communication overhead by sparsifying the model parameters uploaded by clients within the FL framework. Evaluation on a real-world dataset shows that the system achieves an accuracy rate of 84.48%, reduces communication costs by two-thirds, and provides robust privacy protection while maintaining high efficiency.
SBCP-YOLO-R3D: Student Behavior Recognition and Visualization Framework Using Improved YOLO and R3D for Class Video
Real-time recognition and visualization of students’ behaviors in face-to-face classrooms serve as pivotal indicators of learning engagement. However, current methods exhibit limitations in both real-time performance and accuracy. Additionally, in-depth studies have not been extensively conducted to evaluate learning status more conveniently by utilizing computer vision techniques. To address these issues, a novel Student Behavior Recognition and Dynamic Class Portraits Construction framework, named SBCP-YOLO-R3D, incorporating the StB-YOLO and R3D methods, has been proposed to detect student behaviors and construct class portraits. The developed framework comprises two layers: the StB-YOLO detection layer and the R3D classification layer. In the StB-YOLO detection layer, the Lightweight-SEAM (LW-SEAM) is incorporated into YOLOv5 to enhance the recognition of occluded students, by capturing contextual information and enhancing occlusion-related features. Moreover, a Double-SlideLoss function is devised, employing adaptive weighting mechanisms to strike an optimal balance between simple and challenging samples. In the R3D classification layer, the results generated by StB-YOLO are then processed using R3D to produce class portraits. Experiments conducted on the StuAct and SCB-DATASET3-S datasets demonstrate the effectiveness of the StB-YOLO. Compared with the baseline model, StB-YOLO increases the mAP by 3.1%.
Optimizing Bandwidth Allocation in Converged Networks Using Hybrid Deep Learning Model
The demand for high-speed network services and the increasing development of network traffic have led to the popularity of converged networks, which mix various services over a single infrastructure. However, because of the variety of application requirements and resource constraints, ensuring quality of service (QoS) in these networks is difficult. Conventional methods for allocating bandwidth are frequently static, reactive, and inefficient, which results in less-than-ideal network performance. We provide a unique deep learning method to optimize bandwidth allocation in convergent networks in order to overcome this. We create and use three deep learning models: Deep Q-Networks (DQNs), Generative Adversarial Networks (GANs), and a special Long Short-Term Memory-based DQN model. We assess each model’s performance using an extensive dataset. Our results show that the Novel DQN model performs better than the other models in terms of minimum packet loss, increased accuracy, decreased latency, throughput maximization, spectral efficiency optimization, bit error rate reduction, fairness assurance, and effective channel resource use. Better service quality is the outcome of these upgrades, which also significantly increase upload and download speeds. Our empirical studies demonstrate our methodology’s usefulness in real-world scenarios and open the door to intelligent network management solutions that facilitate better QoS, efficient bandwidth allocation, and improved user experiences in converged networks.
Clustering and Mapping of Agricultural Production Based on Geographic Information System Using K-Medoids Algorithm
North Aceh is a district in Aceh Province, Indonesia, with significant potential in agriculture and plantations. Agricultural products, particularly food crops and horticulture, serve as leading commodities, as most of the region is a major producer of agricultural goods. This study identifies the absence of a mapping and clustering system for agricultural production in North Aceh, which complicates the classification of production levels into low, medium, and high categories. To address this issue, this research develops a mapping and clustering system for agricultural production using the K-Medoids algorithm. The system categorizes production into high-production clusters (C1), medium-production clusters (C2), and low-production clusters (C3). The research process includes a literature review, data collection, data analysis, implementation of the K-Medoids algorithm, and geographic information system (GIS)-based mapping. Using 25 test data points from 27 sub-districts in 2023, the study identifies high-production clusters (C1) for commodities such as rice, soybean, mango, starfruit, guava, water guava, Siamese oranges, large oranges, mangosteen, pineapple, mustard greens, soursop, breadfruit, gnetum, twisted cluster bean, and duku. Medium-production clusters (C2) include corn, durian, papaya, banana, rambutan, jackfruit, and jengkol, while low-production clusters (C3) include avocado and snake fruit. These results are visualized through a GIS-based map. The study demonstrates that the K-Medoids algorithm effectively clusters agricultural production in North Aceh, providing valuable insights for local governments and stakeholders to support decision-making in agricultural planning and management for food security.
Emotion Detection Using Machine Learning: A Study on Human Computer Interaction
Emotion is a psychological state of mind that is closely related to various sensations, such as happiness, excitement, neutrality, anger, and frustration. In the modern technological era, emotions play a crucial role in understanding human behavior and motivations. They also have a significant impact on human-computer interactions. If machines can understand human emotions, it would be a remarkable advancement in natural language processing, particularly in the domain of sentiment analysis. Humans experience a wide range of emotions that can change over time. If machines could interpret these emotions and adapt their interactions accordingly, it would greatly enhance the ease and effectiveness of communication between humans and machines. In our study, we employed intelligent algorithms from the field of artificial intelligence, including Naïve Bayes and K-Nearest Neighbor, to interpret human emotions. We utilized speech datasets in the form of audio, sourced from the IEMOCAP database. These algorithms demonstrated the ability to recognize and interpret various emotional states, such as anger, happiness, neutrality, frustration, excitement, and sadness.
Leveraging Transfer Learning for Durian Variety Classification
This study evaluates the effectiveness of transfer learning compared to training a Convolutional Neural Network (CNN) from scratch for classifying eight durian varieties. A CNN with two convolutional layers, two max-pooling layers, and three fully connected layers improves accuracy from 20% to 68.79%, reduces average loss from 2.0 to 0.58, and requires 86 min and 17 s for training. In contrast, transfer learning with ResNet-18 significantly enhances both performance and efficiency. Fine-tuning the pre-trained model increases accuracy from 52% to 94%, stabilizing at 94–96%, reduces average loss from 1.36 to 0.14, and completes training in just 10 min and 20 s. Using ResNet-18 as a fixed feature extractor improves accuracy from 42% to 78%, stabilizing at 78–80%, reduces average loss from 1.68 to 0.68, and completes training in 10 min and 13 s. This study introduces an application-specific adaptation of transfer learning for durian variety classification, bridging a gap in agricultural image analysis research. The findings demonstrate how leveraging pre-trained feature representations enables rapid convergence and robust classification, even with limited data. Additionally, this research provides a foundation for automated durian grading, sorting, and quality assessment, contributing to precision agriculture and smart farming. By benchmarking transfer learning against traditional CNNs, this study highlights the superior efficiency and accuracy of transfer learning for agricultural applications, with broader implications for fruit classification, yield optimization, and post-harvest processing automation.
Enhancing Lung Cancer Detection: Optimizing Deep Learning With Convolutional Block Attention Module
Lung cancer manifests as the uninhibited propagation of cells within tissues in lung. Early exposure of malignant cells within the lungs, which play crucial roles in oxygenation and carbon dioxide exchange vital for human physiology, is imperative. Deep learning in detecting lung cancer has gained considerable attention because of its impending to enhance patient identification of diagnosis and treatment. Nonetheless, existing algorithms demonstrate less-than-ideal performance in crucial areas such as recognition accuracy, precision, sensitivity, F-score, and specificity. To address this issue, a novel method Unet with CBAM is leveraged by the different deep learning pre trained models such as VGG-16, VGG-19, ResNet-50, Resnet-101 and EfficientNet. In this proposed methodology we introduce Unet with Convolutional Block Attention Module (CBAM) for the prediction of lung cancer. The proposed model has a base learner consists of two modules: Unet and CBAM attention. In the attention mechanism there are also two modules: channel attention module and spatial attention modules. The features of each module are integrated and supplied to the meta-learner as a fully connected model for the precision. This model improves results comparison with other models.
Automatic Pricing Mechanism of Pay What You Want Based on Analytic Hierarchy Process-Fuzzy Comprehensive Evaluation Data Value Evaluation
Data trading can improve data circulation and promote the development level of information technology. However, data pricing issues limit the expansion of data trading scale. In response to this issue, this study proposes pricing data products through data value assessment. This study uses the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation method to design a data value evaluation method, which combines the Pay What You Want (PWYW) mechanism with Stackelberg game to design an automated pricing method for data transactions. The results show that the designed data value evaluation method has a value evaluation error of no more than 5% for different data types. When automatically pricing data products based on the value evaluation results, the pricing result should have an error of no more than 5% compared to the market average price, and the pricing result can be automatically adjusted according to market conditions. The proposed automated pricing method for data products can automatically price products based on market conditions, effectively safeguarding the interests of both parties in the data trading market and promoting its development.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by/4.0/
The Role of Artificial Intelligence in Forming Favorable Customer Attitudes on E-Commerce Websites
The research aims to explore the influence of artificial intelligence in increasing technological engagement and attitude toward the firm, which has implications for repurchase intention. In addition, the moderating effect of perceived effectiveness of e-commerce institutional mechanisms (PEEIM) was also explored. A quantitative method approach is used to investigate consumer behaviour from experiences using artificial intelligence technology in e-commerce. Data was collected from 242 e-commerce users using a structured questionnaire and personal interviews. Confirmatory factor analysis and structural equation modelling with the SEM tool AMOS 24 are used to test hypotheses. This research confirms that there is a significant influence between artificial intelligence and technology engagement and customer firm attitudes, contributing to repurchase intentions. The results also support the positive influence of high customer involvement on increasing repurchase intention. However, other results from the attitude toward the firm have an insignificant impact on repurchase intention. PEEIM also shows exciting results regarding its moderation effect. The company continues using AI-based data analysis to understand customer preferences and behavior to provide more relevant and personalized product recommendations. This study is the only one that explores the influence of AI by looking at each of its constituent dimensions, which can increase technology engagement and consumer attitudes towards websites. PEEIM moderation role will also strengthen the latest research results in the field of e-commerce.
Integrating LSTM and CNN for Stock Market Prediction: A Dynamic Machine Learning Approach
This paper investigates the application of machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid LSTM-CNN model, for predicting stock prices of companies listed on the National Stock Exchange (NSE). The proposed hybrid model leverages CNN’s capability for spatial feature extraction and LSTM’s proficiency in modelling temporal dependencies, effectively addressing the complex and volatile nature of stock price movements. Using a comprehensive dataset of historical stock prices and trading volumes from various sectors, the hybrid model achieved superior performance with a 15% improvement in RMSE compared to standalone CNN and LSTM models. Results demonstrate its robustness, particularly in volatile market conditions, showcasing its potential for accurate and reliable predictions. This study contributes a novel hybrid approach that integrates spatial and temporal learning to enhance stock market prediction, offering valuable insights for investors and financial analysts while providing a scalable framework for broader financial applications.
Application of MRI Radiomics Combined With Deep Learning Technology in Glioma Segmentation and Survival Prognosis
Accurate automatic segmentation and prognostic diagnosis based on three-dimensional magnetic resonance imaging (MRI) are essential for the proper treatment of gliomas. We developed a rapid, automated pipeline to segment gliomas and can accurately predict patient survival prognosis based on pretreatment MRI in this paper. T1-Gado MRI sequence images of gliomas are utilized to automatically segment gliomas using deep convolutional neural networks in this study. Nine machine learning models that combine radiomics features and clinical characteristics are leveraged to predict and compare the survival and prognosis of glioma patients. The results of the experiments show that all nine well-known learning model classification architectures can achieve accurate classification and reliable prediction results. The clinical decision curves show that, except for k-nearest neighbor and decision tree, all models perform well at various threshold probabilities.
Deep Learning Multimodal Sarcasm Detection in Social Media Comments: The Role of Memes and Emojis
Social media has become a crucial platform for interaction, information exchange, and market analysis. Businesses and researchers rely on it for sentiment and emotion analysis, yet sarcasm detection remains a major challenge due to its ability to alter sentiment polarity. Traditional text-based analysis struggles with sarcasm as it lacks tone and facial expressions. Additionally, crucial indicators of sarcasm—repeated emojis, punctuation, and characters—are often discarded during preprocessing. To address this issue, we proposed a multimodal deep-learning approach that integrated text, emojis, and images to improve sarcasm detection. This approach preserved and transformed repeated emojis, punctuation, and characters into structured features rather than removing them. Images were processed using Optical Character Recognition (OCR) to extract text to ensure computational efficiency by excluding non-textual visual elements. Word representations were then generated using Word2Vec embeddings, which were fed into LSTM, GRU, and BiLSTM models. The study highlighted the importance of scenario-specific preprocessing and feature selection in sarcasm detection. Among the 15 models tested, LSTM–composite demonstrated stable accuracy and strong generalization (76% accuracy, 73% precision, and 82% recall). Its high computational cost made it unsuitable for large-scale deployment. On the contrary, Model 9 (i.e., BiLSTM–isRepeatedChar) could balance efficiency and predictive performance (76% accuracy, 74% precision, and 79% recall), which made it ideal for resource-limited environments.
DeepSeek vs. ChatGPT: A Comparative Evaluation of AI Tools in Composition, Business Writing, and Communication Tasks
This study presents a comparative evaluation of DeepSeek and ChatGPT, two AI-powered text generation models, in composition, business writing, and communication tasks. The article assesses AI-generated content based on clarity, coherence, adaptability, persuasiveness, and grammatical accuracy, with evaluations conducted by three expert instructors. The findings indicate that ChatGPT outperforms DeepSeek, particularly in linguistic variation, audience awareness, and dynamic content generation. ChatGPT demonstrates superior tone modulation, rhetorical adaptability, and engagement, making it more effective for persuasive messaging, business communication, and content creation. In contrast, DeepSeek excels in grammatical precision, structural organization, and factual consistency, making it a more reliable tool for formal reports and standardized business writing. The study also examines the strategies employed by both AI models, highlighting ChatGPT’s strength in customizing responses to different audiences and business contexts, while DeepSeek often follows a one-size-fits-all approach, resulting in rigid and impersonal writing. Although ChatGPT proves more adaptable, its responses occasionally require refinement for conciseness and formality, whereas DeepSeek would benefit from improvements in creativity, emotional intelligence, and flexibility. The findings underscore the broader implications of AI-driven writing tools in business, education, and professional communication. Organizations can maximize AI’s potential by leveraging ChatGPT for dynamic and persuasive communication while utilizing DeepSeek for structured, technical, and compliance-based writing. Future advancements should focus on enhancing AI adaptability, domain-specific customization, and ethical considerations in AI-generated content, ensuring effective integration into modern professional environments.
Optimizing Waste Management with Squeeze-and-Excitation and Convolutional Block Attention Integration in ResNet-Based Deep Learning Frameworks
Effective waste classification is crucial for sustainable waste management, yet existing automated models face challenges such as misclassification of visually similar waste materials, dataset imbalance, and poor generalization to real-world conditions. This study addresses these limitations by integrating Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) mechanisms into ResNet-50, a deep learning model, to improve waste classification accuracy. Utilizing the TrashNet dataset (six waste categories), we have employed the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset, increasing samples from 2,527 to 3,564. Experimental results demonstrate significant improvements: ResNet-50 achieves 74.42% test accuracy, SE+ResNet-50 improves accuracy to 93.47%, and CBAM+ResNet-50 reaches 95.74%. The findings highlight that attention-based deep learning models can significantly enhance feature extraction, improve classification accuracy, and optimize waste segregation processes, contributing to more efficient recycling and waste management automation. Future work includes extending the model to classify hazardous and mixed waste, ensuring broader applicability in real-world waste management systems.
mU-Net-SAM-ENet: Efficient and Interactive Assistance in Lung Nodule Segmentation
In order to enhance patient recovery, early diagnosis of lung nodules is crucial. Lung cancer continues to be a major worldwide health problem. For lung nodule segmentation, a mU-Net-SAM-ENet is suggested in this work. This work uses a Bayesian threshold-based Taylor series technique for image preprocessing. This method significantly improves contrast and minimizes noise in lung scans, which raises the caliber of the input data. Next, a modified U-Net (mU-Net) architecture, specifically designed for medical picture segmentation, is used in this research. This neural network accurately delineates nodule borders by rapidly capturing complex nodular boundaries. Classification is a crucial stage in the investigation of lung nodules after segmentation. SimAM-EfficientNet, an attention-based deep neural network renowned for its effectiveness and precision in image classification tasks, is used in this method. This research uses the Wildebeest Herd Optimization Algorithm for hyperparameter adjustment to enhance the model’s functionality. The experimental study’s findings demonstrate that the suggested model, employing the LIDR-IDRI datasets from the Image Database Resource Initiative and the Lung Image Database Consortium, attained accuracy values in both segmentation and classification of 99.76% and 98.2%, respectively.
A Hybrid Vision Transformer and Graph Neural Network Model with Attention Mechanisms for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a foremost reason of blindness worldwide due to diabetics, highlighting the need for early and accurate detection to prevent severe vision impairment. However, current DR detection methods often fall short in capturing the intricate relationships between retinal structures and struggle to effectively utilize both local and global features within retinal images. To deal these challenges, this study introduces a novel hybrid structure that combines Vision Transformers (ViTs) with Graph Neural Networks (GNNs), augmented by attention mechanisms, for identification and classification of DR using retinal fundus images. The main objective is to build a robust structure that can accurately capture the complex spatial and temporal relationships within retinal images, thereby improving the precision and reliability of DR detection. The presented approach begins with bilateral filtering during the image pre-processing stage, which preserves essential structural details, such as blood vessels, while reducing noise. ViTs are incorporated to capture higher level features by grouping images into sequences of non-overlapping groups. These features are then used to construct spatial and temporal graphs, enabling the model to capture both detailed local information and broader sequential relationships within the retinal images. The integration of attention mechanisms within the GNNs allows the structure to concentrate on efficient features, further enhancing its detection capabilities. The outcome results illustrates that the hybrid structure outperforms several cutting-edge approaches, achieving an accuracy of 93.2% and an AUC-ROC of 0.961 on the APTOS 2019 Blindness Detection dataset. Ablation studies underscore the significance of attention mechanisms and the synergistic use of spatial and temporal graphs. Despite the structure's strong performance, its complexity and computational demands may limit its feasibility in resource-constrained settings. Future research aims to optimize the structure for such environments and extend its application to other retinal diseases.
Transfer Learning-Based Ensemble Model for Hot-rolled Steel Surface Defect Classification
Hot-rolled steel production is a critical process in the manufacturing industry, with surface defects posing significant challenges for quality control. Accurate classification of these defects ensures product quality and prevents costly rejections. Many studies have focused on employing CNN-based methods to categorize steel surface defects. However, individual CNN models exhibit inherent differences across various aspects, encompassing distinct architectures, differing levels of bias, and variances. On the other hand, individual models may fall short of delivering desirable results when applied to specific datasets. Acknowledging the robust performance shown by ensemble models, coupled with their unique ability to reconcile model bias and variance, this study proposes a new approach using transfer learning techniques to introduce an innovative ensemble model for accurately classifying surface defects in hot-rolled steel. Our proposed ensemble model combines three distinct pre-trained CNN architectures. Each model is individually trained on subsets of defect images to capture diverse features in various defect types effectively. Our results of extensive experimentation on benchmark NEU and X-steel surface defect (X-SDD) datasets indicate that the presented ensemble model outperforms existing methods, achieving classification accuracy of 100% on the NEU dataset and 99.27% on the X-SDD dataset.
A Novel Hybrid Deep Learning Algorithm for Object and Lane Detection in Autonomous Driving
Rapid advancements in deep learning, particularly in object and lane detection, have significantly contributed to autonomous driving technology. Ensuring safe and reliable vehicle operation, and object and lane detection remains a critical component in autonomous navigation. This study reviews deep learning-based models for object and lane detection, proposing enhancements to improve real-time obstacle detection and address existing shortcomings. A robust methodology for data collection, preprocessing, and deep learning-based prediction was developed, integrating CNNs with state-of-the-art object detection models such as YOLO (You Only Look Once) and Single Shot Multibox Detector (SSD). Hough Transform and deep learning methods were applied for lane detection. Publicly available autonomous driving datasets were preprocessed to ensure consistency in accuracy and performance. Experimental evaluations measured model performance using precision, recall, and Intersection Over Union (IoU). We propose a novel two-stage modality-specific model based on the Faster-RCNN framework for an object, lane detection (without anchors), and motion fusion in autonomous vehicles. The model achieved high precision and recall, reducing false positives and improving real-time performance. Comparative analysis shows the proposed deep learning approach is both accurate and computationally efficient. This work has significant implications for the automotive industry, enabling advanced driver assistance systems (ADAS) and accelerating the transition to fully autonomous vehicles. The hybrid approach enhances precision, speed, and flexibility in dynamic environments, ensuring improved perception and decision-making. By integrating object and lane detection into a unified pipeline, the proposed method contributes to the advancement of autonomous vehicle technology and safety.
Federated Learning Model of Optimized Neural Network Structures for Identification and Classification of Cardiovascular Diseases at Multi-Hospital Patients
This research presents a privacy-preserving federated deep learning framework for Electrocardiogram (ECG) classification, enhancing cardiovascular disease diagnosis while maintaining patient data security. Traditional deep learning models require centralized data collection, raising privacy concerns. Our approach trains models locally at individual healthcare centers, preventing raw data sharing. We utilize optimized deep learning architectures, including Optimized Convolutional Neural Networks (CNN), Optimized Recurrent Neural Networks, and Optimized Artificial Neural Networks trained using Genetic Algorithm and Particle Swarm Optimization. Each center trains multiple models, selecting the best-performing architecture dynamically for federated training. Model weights are aggregated using federated averaging (FedAvg) and redistributed to improve generalization. Our hierarchical model selection and aggregation reduce redundancy, enhance scalability, and address challenges like non-IID data distribution and communication overhead. By sharing only model weights instead of raw ECG data, the framework ensures data privacy and compliance with regulations while facilitating multi-institutional collaboration. Experimental results are compared with different architectures and the results demonstrate that Optimized CNN consistently outperforms other architectures, making it an ideal choice for federated ECG classification. This outperforming model is considered as the training model of the individual centers. The trained model weights of individual centers are shared with the data center where FedAverage weights are computed. This research advances privacy-preserving deep learning by optimizing both model performance and security, enabling large-scale, real-world deployment in healthcare systems.
Technical Report Writing Efficiency Using AI-Powered Tools: Opportunities, Challenges, and Future Directions
The study explores the role of Artificial Intelligence (AI) in enhancing the efficiency and quality of technical report writing, focusing on commonly used tools such as grammar checkers, ChatGPT-based drafting assistants, and citation management software. Using a mixed-methods approach, comprising expert interviews and a survey of 83 technical writers, the research examines how AI technologies are currently integrated into professional writing practices and evaluates their impact across efficiency, accuracy, and ethical dimensions. The findings indicate that AI tools offer substantial time savings, especially in automating repetitive tasks such as grammar correction, referencing, and document structuring. They also improve linguistic clarity and consistency in terminology. However, experts caution that AI tools still struggle with domain-specific accuracy and contextual interpretation, limiting their effectiveness in specialized fields like engineering and business. Moreover, ethical concerns, including potential plagiarism, authorship ambiguity, and over-reliance among novice writers, emerge as critical challenges. Despite these limitations, the study finds that AI can play a valuable role as a collaborative partner in technical writing when paired with human oversight. It advocates for developing domain-adaptive AI systems and calls for more straightforward ethical guidelines to support responsible integration. While the research sample is modest and primarily technology-focused, the study offers meaningful contributions to the discourse on AI in technical communication. It provides practical recommendations for leveraging AI tools effectively while preserving the critical role of human expertise, judgment, and ethical responsibility in technical documentation.
Robust Feature Management for Gaming Disorder Classification Using Modified Partial Instance Reduction and Fine-Tune Attribute-Weighted Naïve Bayes
The dynamic nature of the social environment can influence symptom patterns in mental illnesses, thereby affecting their classification. Gaming disorder (GD), recently recognized in ICD-11 by the World Health Organization, is currently identified based on 44 symptoms derived from expert assessments. However, these symptoms may shift as social behaviors evolve, necessitating classification models that remain robust despite changes in dataset attributes. This study proposes a robust model—fine-tuned attribute-weighted Naïve Bayes (FTAWNB) with modified partial instance reduction (mPIR)—to address this issue. Two test scenarios were conducted: the addition of one new attribute and the removal of four attributes from the GD dataset, applied to both the original and updated datasets. The results indicate that FTAWNB with modified PIR enhances classification performance. On the original dataset, accuracy increased by 1.28% (Scenario 1) and 1.4% (Scenario 2). On the updated dataset, the model maintained 99.74% accuracy (Scenario 1) and improved by 1.4% (Scenario 2). These findings demonstrate that the integration of modified PIR improves model stability in dynamic feature environments, thereby contributing to more reliable mental health classification systems.
A Novel Artificial Neural Network Topology to Enhance Combined Cycle Power Plants Modeling Capabilities
Neural networks are prominent among concurrent advanced technology techniques due to their capacity to deal with massive long-term datasets and the nonlinear modeling in combined cycle power plants. This paper studies an adaptation of the Levenberg–Marquardt training algorithm to train and assess a combined cycle power plant output. The most robust electric power predictions are identified for 70% of the training data, 15% of the validation, and 15% of the testing data. The sensitivity impact of reducing the design variables to two and three via a modern methodology for the electric power estimation at a reduced cost constraint is presented. The adapted actual experimental data (9568) from six years of four input parameters, ambient temperature, exhaust vacuum, ambient pressure, and relative humidity, were used. These input parameters were combined in different datasets as the ambient temperature (P1), the exhaust vacuum (P2), and the ambient pressure (P3), applied to different settings using (P1+P2, P1+P3, P2+P3) for the two input variables and (P1+P2+P3) for the three variables. The implementation of the Levenberg–Marquardt training code for a hidden layer size of (20, 500) contributes to the output power prediction. The regression values obtained for the two variables combined datasets (P1+P2, P1+P3, and P2+P3) were 0.9701, 0.9658, and 0.9401, thus highlighting the superiority of the (P1+P2) dataset. When the design variables were increased to three (P1+P2+P3), a better prediction of electric power in terms of the improved regression value 0.971 was observed with a mean square error of 13.8389. These mean square error and regression coefficient values for both network settings (P1+P2) and (P1+P2+P3) showed improved performance compared to past studies. Hence, this new approach of neural network configuration with the three input parameters provides a novel prediction of the output power, which can be used to validate the combined cycle power plants and has computational benefits in other real-world applications.
Three Large Language Models for Solidity Smart Contract Vulnerability Detection
In decentralized apps, smart contracts are used to conduct trusted transactions on the Blockchain (BC). While smart contracts are highly effective, they are also highly susceptible to security flaws, leading to serious financial consequences. However, the combination of BC technology and artificial intelligence provides a solution for powerful, secure, and decentralized applications in various sectors. Furthermore, large language models (LLMs), which are essential advanced machine learning frameworks, are now used in various applications, including customer service, chatbots, code generation, vulnerability detection, and language translation. This study investigates the use of LLMs for automated vulnerability detection in Solidity-based smart contracts. Specifically, three models are evaluated and compared: GPT-3.5-turbo, DeepSeek R1, and LLaMA-3. With a labeled, multi-class dataset including four vulnerability types, the models are assessed across three reasoning strategies: zero-shot, few-shot, and chain of thought. A prompt-based evaluation and performance comparison is conducted using standard metrics such as accuracy, precision, recall, F1-score, and average detection time. Results show that in the zero-shot setting, GPT-3.5-turbo achieves the highest accuracy of 94.59%, followed closely by LLaMA-3 with 92%, while DeepSeek R1 achieved 78.95%. In the few-shot setting, LLaMA-3 outperformed other models. Furthermore, in the CoT setting, LLaMA-3 demonstrates the strongest overall performance with 96% accuracy and an F1-score of 0.82, surpassing DeepSeek R1's average of 78.95% and GPT-3.5's CoT performance, which is notably lower. Hence, this study develops an evaluation framework for LLM-based vulnerability detection, and we have demonstrated that prompt engineering has the potential to enhance the security of smart contracts.
Optimization of Test Path for Microfluidic Biochip Design Using Genetic Algorithm Combined with Particle Swarm Optimization Algorithm
To enhance the efficiency and precision of microfluidic biochip testing, this study proposes a hybrid optimization model integrating genetic algorithm (GA) and particle swarm optimization (PSO) with a dynamic priority strategy. Traditional methods, such as the ant colony algorithm and Euler loop method, often suffer from slow convergence or local optima in complex path optimization scenarios. By combining the global exploration capability of GA and the local exploitation strength of PSO, the proposed GA-PSO algorithm dynamically adjusts search priorities to minimize interference between experimental and test droplets. This approach optimizes test paths for both offline and online testing modes. Experiments on chips ranging from 7 × 7 to 15 × 15 arrays demonstrate significant improvements: the 15 × 15 chip achieves a shortest path length of 442 in both modes, reducing iterations by 60.9% for offline and 16.9% for online testing compared to standalone GA or PSO. Compared to the ant colony algorithm and Euler loop method, the proposed method shortens offline test paths by 4.91% and 5.56%, respectively, and online test paths by 8.98% and 9.80%. Key contributions include (1) a novel hybrid algorithm balancing global and local search, (2) a dynamic priority strategy mitigating droplet interference, and (3) a universal framework applicable to diverse chip specifications. These advancements offer practical guidance for real-time detection and batch processing in biomedical engineering, significantly improving testing efficiency.
Improving Skin Disease Classification with a Filter-Based Ensemble Feature Selection Framework
Skin diseases are increasingly concerning due to environmental changes, with dermatology emphasizing the need for early diagnosis. Classification in data mining is a key approach to predicting skin diseases by analyzing datasets. Before applying classification techniques, identifying relevant attributes is crucial. Feature selection (FS) is critical in preprocessing to identify relevant attributes and avoid redundant or irrelevant data. While wrapper methods offer better accuracy, they are computationally expensive. Our proposed framework aims to emulate wrapper-like feature subset evaluation using an efficient filter-based approach, leveraging Symmetrical Uncertainty (SU) and classifier-driven subset validation to achieve improved performance without excessive computational cost. Features are grouped into subsets, minimizing the dataset’s complexity while retaining predictive power. Subsets are evaluated using classifiers such as K-Nearest Neighbour (KNN), JRip, Naive Bayes, and J48, and compared against traditional filter-based techniques like Relief (REL), Gain Ratio, Chi-Squared (Chi), and Information Gain. The ranking is assigned based on classifier performance. The proposed framework showed significant improvement. One or more feature subsets consistently ranked in the top 4 across all classifiers compared to existing methods. For instance, subsets formed with SU achieved an accuracy boost of 91.53% with J48 and 91.25% with KNN in a dermatology dataset.
Interpretable Deep Learning for Enhanced AI Trust and Clarity
This research aims to explore the role of interpretability in increasing user trust in artificial intelligence (AI) systems through tools such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). The study stands out with its approach of comparing the effectiveness of two popular interpretability techniques in bringing transparency to deep learning models, particularly in high-risk applications such as health and finance. The research method involves applying the interpretability tools to AI models and evaluating user confidence and perception of transparency using feature visualization. Results show that interpretability has been significant in increasing user confidence, with SHAP excelling in providing global interpretation and LIME providing clarity on specific predictions. Visualizations proved effective for nontechnical users in understanding model decisions, although computational efficiency challenges remain, especially with SHAP. In conclusion, interpretability supports the ethical use of AI by increasing accountability and accessibility, and it demonstrates the importance of selecting interpretability tools based on context and user needs. The results provide practical direction for AI developers in integrating interpretability from the design stage to ensure transparency and reliability.
A Multimodal Framework for Speech Emotion Recognition in Low-Resource Languages
Speech emotion recognition (SER) plays a crucial role in enhancing human–computer interaction by identifying emotional states in speech. However, low-resource languages like Kazakh face challenges due to limited datasets and linguistic tools. To address this problem, we propose a novel multimodal framework, KEMO (Kazakh Emotion Multimodal Optimizer), which combines text-based semantic analysis and audio emotion recognition to leverage complementary features of linguistic and paralinguistic data. Using a Kazakh-translated version of the DAIR-AI (Contextualized Affect Representations for Emotion Recognition) dataset for text and the RAVDESS (Ryerson Audio-Visual Database of Emotional Speech and Song) dataset for audio, we have developed a system capable of classifying six emotions from text and eight emotions from audio. By integrating outputs from speech-to-text and audio-based recognition models with adaptive weighting, KEMO significantly improves the accuracy and robustness of emotion classification, providing an effective solution for SER in low-resource language scenarios.
Effects of Analytics Large Data Set on Decision-Making and Organizational Performance: A Study on Chinese Manufacture Sector
In today’s data-driven environment, Big Data Analytics (BDA) plays a vital role in enhancing decision-making quality and organizational performance. However, limited empirical research exists on how the five characteristics of big data (5Vs: Volume, Velocity, Variety, Veracity, and Value) influence decision-making effectiveness in China’s industrial sector. Addressing this gap, the present study builds on Simon’s decision-making theory and the information processing perspective to develop and test a research model linking BDA to decision-making and performance outcomes. Using a self-designed structured survey, data were collected from 312 managers across medium and large-sized manufacturing firms in China. Structural equation modeling (SEM) was employed to examine the relationships among constructs. The results show that all five BDA characteristics significantly enhance the quality and efficiency of decision-making, which in turn positively impacts organizational performance. Furthermore, multi-group analysis revealed no significant difference in the BDA–decision-making relationship between medium and large enterprises. This study contributes theoretically by integrating BDA with decision-making theory and practically by offering managers evidence-based insights on how to leverage big data for more informed and effective decision-making across industrial operations.
Evaluation of Dragon and Lion Dance Teaching Actions and Digital Sports Intangible Cultural Heritage Inheritance Based on Hypergraph Convolution
Given that traditional graph structures make it difficult to capture complex interaction information between entities, this study adopts a hypergraph model to represent multimodal and heterogeneous data, to adapt to the complexity of dragon and lion dance movements. This study proposes a new method based on a hypergraph convolutional network (HGCN) for the inheritance and teaching action evaluation of the intangible cultural heritage of the dragon and lion dance. This method constructs the HGCN model, combined with a self-attention mechanism to accurately evaluate action details and promote its inheritance in the digital age. The results show that the HGCN algorithm incorporating the attention mechanism exhibits excellent performance, the accuracy achieves 0.941, the error rate reduces to 0.333, an evaluation efficiency improved by 400%, and user satisfaction increases to 0.900. These results not only validate the efficiency and accuracy of the model but also demonstrate its potential to improve the teaching and inheritance efficiency of the dragon and lion dance. This study not only provides a new technological means for the digitization of intangible cultural heritage in sports but also opens up new paths for the modern teaching and inheritance of traditional sports projects.
Identification of Emotional States and Psychological Crisis Prevention for Medical Students Based on Integrated Learning and Walking Pattern
Currently, the psychological crisis prevention methods for medical students combine with artificial intelligent technology and mainly analyze students’ facial and electroencephalogram (EEG) signals. These methods have the problems of poor operability and low recognition accuracy. To address these problems, the study proposes a method for psychological state recognition and psychological crisis prevention based on ensemble learning and walking pattern analysis. Moreover, the study applies this method to the psychological crisis prevention of medical students. First, the study uses a pose estimation algorithm and a convolutional structural network to extract 3D features of walking patterns. Then, it applies these features to emotion recognition. The research selects K-nearest neighbor, decision tree algorithm, and support vector machine as the integrated weak classifier. The experimental results indicated that the recognition accuracy of the psychological state recognition method proposed in the study reached 95.63%. The response time was only 0.23 s, with a recall value of 0.96 and an root mean squared error of 0.03. In the practical application of the psychological crisis intervention, the accuracy of its early warning reached 91.48%, and the rate of improvement of the students’ psychological state after the early warning also reached 78.23%. The proposed method can help teachers gain timely insight into the psychological problems of students and carry out psychological crisis prevention.
Building a Large Language Model for the Human Resource Services in Malaysia
This study presents the development of a domain-specific base large language model (LLM) tailored for its application in human resources (HR) management, particularly employee engagement. The model addresses inefficiencies in traditional HR systems, such as inconsistent query resolution on compliance management and policy dissemination. By leveraging LLM’s advanced techniques like Rotary Positional Embeddings (RoPE), grouped key-value attention, and Transformer Block enhancements, this research designs and develops an HR-specific base LLM as the first line of HR service for employees in small- and medium-sized enterprises. Data preparation involves cleaning, tokenizing, and training HR-specific datasets, enabling the model to handle nuanced queries with contextual relevance. Through iterative training and evaluation, the Enhanced GPT-2 model has demonstrated significant improvements in learning capability, based on attention weights in the embedding layers, over GPT-2 Small in terms of relevance, consistency, and scalability. Future work focuses on expanding datasets, improving fine-tuning techniques, and integrating retrieval-augmented generation for real-time adaptability.
Assessment of Students’ Entrepreneurial Intention Based on Residual Networks and Attention Mechanisms in the Process of Entrepreneurship Education in Colleges and Universities
With the rapid development of artificial intelligence technology, its application in the field of education has gradually become a research hotspot. Aiming at the problems of strong subjectivity and poor assessment accuracy of traditional entrepreneurial intention assessment methods in entrepreneurship education (EE), the study proposes a method for assessing students’ entrepreneurial intention based on residual network and multi-attention mechanism. The method captures the local features of student data through multi-scale convolution and captures the global dependencies using the Transformer model, so as to realize the complementarity and fusion of features. On this basis, the study further introduces a multi-attention mechanism to weigh the features from multiple perspectives in order to capture the relationship between the features more comprehensively. Through the combination of the improved residual network and the multi-attention mechanism, an efficient model for assessing students’ entrepreneurial intention is constructed. The results indicated that the accuracy of the proposed model was as high as 0.923, which was improved by 16.7% compared with the traditional model, the area under the curve was improved by 13.0%, and the recall and F1 score of the model were increased by 17.2% and 15.4%, respectively. The results showed that the method outperformed all existing methods and provides data support for EE in colleges and universities by accurately assessing students’ entrepreneurial intentions. The results can help colleges and universities optimize their curriculum, resource allocation, and teaching mode, thus enhancing the overall efficiency of EE.
Video Anomaly Detection in Crowded Scenes Using Deep Learning
Detecting abnormalities accurately in crowded settings remains a vital problem with many real-world applications, such as crowd video surveillance and crowd behavior analysis. For detecting anomalies in such situations, conventional techniques such as Optical Flow (OF), Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT) have been applied, observing their computational complexity and the dynamic nature of the crowd behavior. The introduction of sophisticated deep learning techniques observes its impact on video surveillance systems protecting the public from heedless violent and illegal activities like robberies, thefts, fights, and vandalism. The suggested technique implements a novel deep learning method for video anomaly identification in crowded settings, applying Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs) to extract abnormal temporal and spatial information from the UCSD dataset’s video sequences. YouOnlyLookOnce version4 (YOLOv4) accurately identifies and detects anomalies in the processed video frames cohesively with bounding box predictions. The Deep SORT tracking algorithm tracks the anomalies with the detected and computed input weights, preserving their distinct tracking identifications (IDs). With an accuracy value of 99.8%, experimental findings on the UCSD Ped2 dataset show that this method performs better than state-of-the-art techniques such as DTA, Ensemble Learning, and RNN-LSTM.
Federated Deep Learning-Based Security Model Against DDoS Attack for Cloud Environment
Technological advancements on the internet have joined the realms of space, air, land, and sea warfare with a substantial impact on both personal and national security. Distributed denial of service (DDoS) attacks cause a denial of service for legitimate users by flooding a targeted network or service with large volumes of traffic from multiple hacked systems. This traffic dataset falls under the Bigdata category as DDoS attacks can generate a large amount of traffic in a brief period. Cloud-based networks and services are under threat from these attacks. Traditional machine learning-based DDoS attack detection lacks scalability, latency due to huge, centralized data requirements, and corresponding security concerns. Federated learning (FL)-based security model addresses these issues without sharing raw traffic data required for the training step. This paper proposes an FL-based security model for enhancing FL security in a cloud environment while preserving data privacy by default. The model focuses on trust assumption, FL protection mechanism, and trust boundaries using zero-trust principles. FL-based security model ensures CIA (confidentiality, integrity, and authentication) during the model training using CRYSTALS-Kyber post-quantum cryptographic algorithm, which is a lattice-based algorithm for key exchange. Even though it supports encryption, we use Advanced Encryption Standard (AES) for confidentiality. Simulation scenarios study the behavior of the system under different conditions to understand the effects of a DDoS attack on a cloud computing environment. The model achieves high DDoS detection accuracy with reduced communication overhead.