International Journal of Information and Communication Technology (5 papers in press)

Regular Issues

• Open Access

  Early warning of college students ideological public opinion based on TF-IDF and RFB neural network ( Free Full-text Access )

  by Guixue Tan 
  Abstract: To address the need for real-time early warning of college students social media opinions, this study proposes a dynamic model integrating term frequency-inverse document frequency (TF-IDF) feature weighting and radial basis function (RBF) neural networks. A subset of 32,715 college-student comments from Tsinghua Universitys Weibo-100k dataset serves as training samples, with cross-domain validation performed using the ChnSentiCorp benchmark. The approach optimises text feature sparsity via TF-IDF and utilises the nonlinear classification capability of RBF networks for opinion risk categorisation. Experimental results demonstrate an F1-score of 89.7% on the test set - marking a 6.2% improvement over conventional long short-term memory networks - while reducing warning response latency to 12 ms. This confirms high accuracy and real-time performance, providing a lightweight solution for monitoring campus ideological dynamics.
  Keywords: public opinion early warning; TF-IDF features; radial basis neural network; college students’ thought dynamics; social media analysis.
  DOI: 10.1504/IJICT.2026.10076066
   
  

• Open Access

  College students career planning for the development of low-carbon renewable energy economy ( Free Full-text Access )

  by Yunwei Dong 
  Abstract: This study examines the impact of low-carbon renewable energy economic development on college students' career planning. Findings reveal rapid industry growth but a significant talent shortage, with only 15% of students considering careers in this sector due to limited awareness. The paper proposes enhancing industry promotion, improving relevant knowledge and skills, expanding internships and employment channels, and calls for governmental and societal support to foster sustainable industry growth and talent cultivation.
  Keywords: low carbon economy; employment; renewable energy; market research; career planning; survey research.
  DOI: 10.1504/IJICT.2026.10076117
   
  

• Open Access

  The synergy of educational resource allocation and teacher motivation based on NSGA-II model ( Free Full-text Access )

  by Xueyao Wang, Jin Xu 
  Abstract: This article proposes a collaborative optimisation model for educational resource allocation and teacher incentive mechanism based on NSGA II. By simulating various allocation and incentive strategies, the model quantitatively analysed their interactions. The results indicate a significant synergistic effect: optimising coordination can improve student performance and teacher efficiency. Once the incentive intensity reaches the threshold, the teaching quality and teacher participation significantly improve, while the turnover rate decreases. Research has shown that combining appropriate resource allocation with incentive design can effectively improve educational outcomes. This method provides a scientific basis for resource allocation, offers a new perspective for incentive mechanism design, and has significant practical application value.
  Keywords: NSGA-II model; educational resource allocation; teacher motivation; multi-objective optimisation; quality of teaching.
  DOI: 10.1504/IJICT.2026.10076180
   
  

• Open Access

  Multimodal deep learning for evidence assessment with algorithmic bias analysis in criminal law ( Free Full-text Access )

  by Huishan Li 
  Abstract: This study integrates multimodal deep learning techniques for evidence assessment to investigate algorithmic fairness in the criminal justice system. The proposed approach predicts criminal charges and evaluates bias related to age and ethnicity by analysing demographic data, online crime reports, and historical records. Convolutional and recurrent neural networks with fairness-aware regularisation are employed to balance equity and predictive accuracy. While algorithmic crime prediction can assist judicial decision-making, it often faces criticism for bias, limited transparency, and lack of interpretability. The primary objective of this research is to predict charge severity while ensuring fairness and transparency. Prior studies have emphasised deep learning applications in fairness-aware algorithms and legal decision prediction, as well as potential racial bias in tools like COMPAS. Using extensive government statistics and crime narratives, ConvLSTM and Bi-LSTM models achieved superior performance, with macro-average F1 scores up to 0.86, while fairness regularisation reduced demographic disparities.
  Keywords: algorithmic crime prediction; deep learning models; bi-LSTM/RNN; ConvLSTM architecture; neural network classifiers; risk assessment instruments; RAIs; algorithmic fairness; bias in criminal justice.
  DOI: 10.1504/IJICT.2026.10076181
   
  

• Open Access

  Optimal allocation model network of human resources in energy enterprises based on NSGA-II ( Free Full-text Access )

  by Zhenhua Fan 
  Abstract: Against the backdrop of the dual carbon; goal and the intelligent transformation of energy systems, energy enterprises face core human resource allocation challenges, including 60% fluctuations in demand between peak and valley, multi-objective optimisation, and complex skill matching. Traditional static methods lead to a 40%-50% labour cost ratio and an over 35% skill mismatch rate. This study proposes a dynamic optimisation model with a forecasting-optimisation-real-time adjustment closed-loop framework, adopting ARIMA-GARCH (+-7% error) and IE-NSGA-II for four-objective optimisation. Empirical tests on a provincial power grid show the model reduces labour costs and carbon emissions by 17.3% and 10.5%, respectively, while improving efficiency and satisfaction by 21.8% and 18.6%, respectively.
  Keywords: Against the backdrop of the ‘dual carbon; goal and the intelligent transformation of energy systems; energy enterprises face core human resource allocation challenges; including 60% fluctuations in demand between peak and valley; multi-objective optimisation; and complex skill matching. Traditional static methods lead to a 40%–50% labour cost ratio and an over 35% skill mismatch rate. This study proposes a dynamic optimisation model with a ‘forecasting-optimisation-real-time adjustment’ closed-loop framework; adopting ARIMA-GARCH (±7% error) and IE-NSGA-II for four-objective optimisation. Empirical tests on a provincial power grid show the model reduces labour costs and carbon emissions by 17.3% and 10.5%; respectively; while improving efficiency and satisfaction by 21.8% and 18.6%; respectively.
  DOI: 10.1504/IJICT.2026.10076333