Convolutional neural networks (CNNs) are widely used in computer vision for tasks like image classification and detection. These models work well when the number of image classes is small, but as the number of classes increases, accuracy tends to drop due to overfitting. There are several methods to address this issue, such as data augmentation, preprocessing, class weighting, transfer learning, and adjusting technical parameters. This study introduces a novel approach utilizing the theory of inventive problem-solving (TRIZ) methodology to systematically analyze and enhance these existing methods. Using reverse engineering, we deconstructed current solutions and aligned them with TRIZ principles to propose more innovative and effective approaches for improving CNN performance. The results show that TRIZ provides a structured and creative framework for solving accuracy decline issues in CNN models, offering the potential for broader applications in other machine learning architectures.

This paper introduces the Mayan calendar-inspired cyclical theory of inventive problem solving (TRIZ) model, an innovative approach to systematic innovation (SI) that integrates the seven TRIZ pillars into a structured model consisting of “Tzolk’in” (short-term, adaptation), “Haab” (mid-term, harmonization), and “Long Count” (long-term, transformation) cycles. Unlike traditional linear innovation models, this cyclical model enables continuous adaptation, iterative refinement, and sustainable evolution. Each cycle addresses a different level of complexity: The adaptation cycle focuses on rapid, low-cost improvements using available resources. The harmonization cycle resolves deep-rooted contradictions to enhance system functionality. The transformation cycle drives strategic evolution by integrating intelligence and automation. This approach is validated through its alignment with trends of engineering system evolution, demonstrating that innovation naturally progresses through these phases. The model’s practical applicability is illustrated through case studies on coffee machine design and automotive seat design, showing how short-term enhancements, mid-term optimization, and long-term transformation collectively contribute to sustainable evolution. By bridging systematic problem-solving with iterative adaptation, the cyclical TRIZ model provides a versatile and scalable SI model for industries seeking to achieve both immediate efficiency gains and long-term innovation resilience.

A typical traffic environment in an intelligent transportation system (ITS) involves various infrastructural units that generate a vast amount of sophisticated traffic data. Such a form of complex data is challenging to analyze and hence poses a potential issue in designing an effective and responsive traffic management system. Therefore, this paper develops an analytical modeling approach to harness the potential of artificial intelligence and computational intelligence. The scheme presents a simplified predictive approach that is meant to mitigate current issues and promote intelligent traffic management. The simulated outcome of the study showcases that the proposed scheme offers a significant advantage in its predictive performance in ITS.

India is emerging as a key destination for global automobile makers, prompting businesses to improve their abilities in product design and development to grow within the technology-focused automobile sector. Managing new product development (NPD) poses significant challenges within the dynamics to remain competitive. A well-defined and proven NPD process in the automobile industry results in high-quality, cost-effective, and timely product delivery to the market. Various frameworks have been proposed in the literature, and limitations highlight the need for a more flexible, integrated, and adaptive NPD model. Utilizing Cooper’s highly efficient Stage-Gate framework, this research proposes a new NPD process framework to enhance the performance of the automobile industry. Based on the limitations of existing stages and gates used and a survey among the NPD professionals, detailed activities of the stages and associated gates have been presented.

Machine-generated text presents a potential threat not only to the public sphere but also to education, where the authenticity of genuine students is compromised by the presence of convincing, synthetic text. There are also concerns about the spread of academic misconduct, particularly direct replication among students. In response to these challenges, this paper introduces the Handwriting Match and Artificial Intelligence (AI) Content Detection System (HMAC). HMAC utilizes optical character recognition (OCR) mechanisms to convert handwritten and typed content from a single-page portable document format into machine-readable text, thus enabling further analysis. Drawing on recent advances in natural language understanding, HMAC aims to preserve the educational value of assignments by effectively detecting AI-generated content. In addition, HMAC has a strong plagiarism detection system that uses a comparative analysis of student submissions in a particular academic field. This paper describes HMAC’s architecture, methodology, and results, emphasizing its key contributions: improved handwritten content extraction with OCR and improved identification of AI-generated content. The study addresses the research question of how HMAC improves the identification of AI-generated content and supports academic integrity compared to other methodologies.

This study explores the integration of artificial intelligence (AI) tools in healthcare, focusing on their impact on cognitive workload, decision-making, and professional development. The findings indicate that AI tools significantly reduce cognitive load, enabling healthcare professionals to focus on higher-order tasks such as critical thinking and complex problem-solving. A majority of participants reported that AI positively influences their professional development, enhancing cognitive functions and empowering them in clinical decision-making. However, concerns were raised about AI’s potential negative effects on hands-on clinical skills, particularly in areas such as physical examinations and surgeries, which require manual expertise. These concerns align with the theory of “skill degradation,” where over-reliance on AI may hinder the development of essential practical skills. In addition, the study revealed that healthcare workers feared AI could reduce their autonomy in decision-making, emphasizing the need for maintaining human oversight in AI-driven processes. The findings suggest that a balanced approach to AI adoption is essential, where AI complements human expertise rather than replacing it. Training programs should be developed to ensure that healthcare professionals retain core competencies while utilizing AI effectively. Overall, while AI has the potential to improve healthcare delivery by enhancing efficiency and supporting decision-making, its integration must be managed carefully to preserve the essential role of healthcare professionals in providing high-quality care.

Brain tumors represent one of the most extreme and complex types of cancer, requiring unique analysis for powerful remedy and management. Accurate and early identification of brain tumors can greatly enhance patient outcomes and decrease mortality. Nowadays, deep learning aids the medical field a lot by diagnosing magnetic resonance imaging images in brain tumors. The potential of deep learning architectures to improve brain tumor diagnosis accuracy was explored in this work. This study evaluated three different convolutional neural network architectures: AlexNet, VGG16, and ResNet18 as an ensemble model. By leveraging the complementary strengths of these models and applying them to a dataset sourced from local hospitals and public repositories, this research aims to address the challenges in accurate and early brain tumor detection. Our ensemble technique achieved excessive accuracy, demonstrating its potential for reliable computer-aided diagnosis (CAD) in medical imaging. However, while the results indicate an improvement in class overall performance, the novelty of this approach is restrained because it builds upon existing methodologies as opposed to offering a completely new framework. The gathered dataset was used to train and test the models. To enhance the dataset’s balance and the models’ performance, data were collected from Rizgary Hospital (Erbil) and Hiwa Hospital (Slemani), addressing the underrepresentation of cases from the Kurdistan Region of Iraq (KRI). These image enhancement techniques were applied to two categories: normal and abnormal brain tumors. Several brain tumor datasets are available online for the development of CADs, but not KRI cases, which pose challenges in their classification through deep learning models. This study was implemented with Python programming language. Out of the three models, ResNet had the highest accuracy of 98.66%, VGG16 had an accuracy of 97.8%, and AlexNet had an accuracy rate of 97.666%. The ensemble, using both majority voting and weighting voting strategies, achieved an accuracy of 98.33%.

Digitalization plays an essential role in improving company performance, including banking. Understanding consumer sensitivity in digital banking applications is essential for strategic decisions. This research aims to analyze the user’s sensitivity of the largest state, first mover, and largest private bank digital application in Indonesia, using the Naïve Bayes technique through Python. The data were taken from the Google Play Store, a software provider application for computer/laptop and mobile users, with a time range of 3 months from April 2023 until July 2023. The applications as the subject were XAA because it is owned by the largest state-owned bank, namely Bank XA, XBB because it belongs to the first mover for digital banks, and XCC, which is an application from the largest privately owned bank, namely Bank XC. The results reveal that most digital bank application users in Indonesia perceive that existing digital bank applications in Indonesia have yet to be able to meet their expectations. This is explained by the higher average negative value of their feedback answers than the existing positive value. Furthermore, this conclusion was revealed from the finding that XAA and XCC, which still had a positive score, had a higher negative score. Meanwhile, the XBB application, which is a first mover, was found to have a positive value higher than a negative one. We clearly compare the three applications divided into positive and negative categories and discuss the existing negative comments using a Digital Business Capabilities perspective.