International Journal of Systematic Innovation

TRIZ reverse-based new application identification for low-density, high-strength thin cement sheets

Jyhjeng Deng — 2025-08-15

This paper explores the application of TRIZ Reverse methodology to the identification of new markets for patented products, with a focus on low-density, high-strength thin cement sheets. TRIZ Reverse is a process in which known solutions are used as a basis for seeking new problems or applications, and involves steps such as back-tracing product strengths to inventive principles, selecting catchwords, conducting database searches, analyzing patent lists, and identifying opportunities for patent exploitation. Although research in the literature has used these methods, the details are often kept confidential. This study bridges that gap by offering a detailed examination of the TRIZ Reverse process and its application to cement sheets, with specific demonstrations of patent search commands and a discussion of potential exploitation avenues. The insights provided here can facilitate a broader understanding and implementation of TRIZ Reverse, thus empowering researchers to identify untapped market opportunities for existing technologies.

Strengthening the Absorptive Capacity of National Innovation System Through University Industry Research Collaboration: A TRIZ Approach

Abeda Muhammad Iqbal — 2025-08-15

There has been a universal recognition that university-industry research collaboration (UIRC) is vital to strengthen the national innovation system (NIS) and economic growth. Despite series of research evidences of the significance of (UIRC), current baseline models to enhance the capabilities of NIS through URIC is still scarce, specifically in developing countries. This research has highlighted that absorptive capacity has a vital influence national innovation system as well as on research and innovative activities of an individual. Moreover, this research highlights how education and training can enrich absorptive capacity of (NIS) and (UIRC) using the Theory of Inventive Problem Solving (TRIZ) approach. The methodology involves applying TRIZ tools such as function modelling, contradiction analysis, and inventive principles to identify effective strategies for improving absorptive capacities of universities and industries and consequently of NIS. Thus, proposed solutions include enhancing education and training system and programs, promoting collaboration between universities and industries, and decreasing aids like foreign educated and skilled workforce (FESW) can strengthen the absorptive capacity of NIS. Analysis of this research suggests that strong education and training system and upgrading standard of education are crucial factors for improving absorptive capacity of NIS. Recommendations include developing policies that fostering a culture of knowledge, promoting interdisciplinary research and incentivize innovation. Future research directions include exploring the comparative analysis with other developed and developing country’s strategies in customizing the education and research system to enhance the outcomes of research and innovations.

Detection of Lung Cancer Mutation Based on Clinical and Morphological Features Using Adaptive Boosting Method

Lailil Muflikhah — 2025-08-15

Lung cancer is one of the leading causes of cancer-related mortality worldwide, with mutation detection playing a critical role in personalizing treatment strategies. Identifying Epidermal Growth Factor Receptor (EGFR) mutations non-invasively remains challenging due to the complex clinical and morphological patterns in patients. This study aimed to develop an Adaboost-based machine learning model to detect lung cancer mutations using clinical and morphological data from patients. Our contribution includes a novel application of the Adaboost algorithm to analyze clinical and morphological features, providing an efficient, non-invasive alternative for mutation detection. The dataset included clinical attributes and morphological data from 80 patients, processed through various preprocessing techniques such as imputation, outlier removal, and feature selection. Data were split into training and testing sets with an 80/20 ratio, and Adaboost was trained with optimized hyperparameters to maximize accuracy and robustness. The experimental results showed that Adaboost outperformed other machine learning algorithms, achieving high accuracy and stability across all preprocessing scenarios. After feature selection using ANOVA, Adaboost achieved an accuracy of 83% and an AUC of 0.90, indicating its robustness and sensitivity in mutation detection. The model was effective even when outliers were removed, and it maintained superior cross-validation scores compared to Naive Bayes, Decision Tree, KNN, and SVM. In conclusion, the Adaboost algorithm proved to be a reliable approach for detecting lung cancer mutations based on clinical and morphological data, offering potential as a supportive tool in clinical decision-making.

Single Frame Super resolution with Deep Residual Network - Generative Adversarial Networks

jayanth — 2025-08-15

Develop and evaluate a deep learning-based method to enhance satellite image resolution, addressing challenges posed by motion, imaging blur, and noise without modifying existing optical systems. The study utilized an enhanced super-resolution generative adversarial network (SRGAN) with ResNet-50 as the generator and a modified VGG-19 in the discriminator. The model was trained on remote sensing images from LISS imagery and compared with VDSR, SRGAN, and ESRGAN methods using SSIM and PSNR as evaluation metrics. Utilizing an enhanced SRGAN with ResNet-50 and modified VGG-19 significantly improves satellite image resolution. The proposed method consistently outperformed traditional CNN and GAN-based super-resolution techniques. Across three test datasets, the method achieved SSIM scores as high as 0.862 and PSNR scores of 33.256, 32.886, and 34.885, demonstrating its superior ability to preserve image properties and enhance resolution. The incorporation of perceptual loss alongside pixel loss contributed to improved visual quality, making the approach particularly effective in maintaining fine details and naturalistic high-frequency characteristics.

Harnessing mobile multimedia for entrepreneurial innovation and sustainable business growth

Rishu Bhardwaj — 2025-08-15

This research investigates the role that mobile multimedia platforms and artificial intelligence (AI) as a technology adopts has would have on innovation as well as on the sustainability of entrepreneurial businesses, looking in particular at the acquisition of technology, its integration and the infrastructure. For data collection, the study employed a quantitative research design and a survey of 150 Indian technology firms which adopted mobile multimedia applications. Using Structural Equation Modeling (SEM) the analyst described his findings with a variety of descriptive measures, correlation, regression analysis, and the mix in attempt to understand the adoption and use of digital technologies for innovation activities. The results show that AI driven applications, combined with multimedia content and real-time analytics significantly enhance entrepreneurial innovation by improving operational efficiency, increasing customer engagement, and expanding into new international markets. Companies utilizing mobile multimedia platforms achieve competitive advantage, which translates into long-term business growth and sustainability. This research adds to the knowledge of AI entrepreneurship in the context of digital transformation by highlighting the necessity for startups to focus on investing in AI enabled mobile technologies. It equally serves policymakers to regulate an environment that promotes innovation and business sustainability through digital initiatives. This research fills a very pertinent gap in the literature by providing evidence on how AI serves as a driver for change and providing insight into the adoption of new technologies in the context of entrepreneurship which is largely absent in the existing literature.

Decoding Marathi Emotions: Enhanced Speech Emotion Recognition via Deep Belief Network-SVM Integration

Varsha Nilesh Gaikwad — 2025-08-15

SER in Marathi presents considerable hurdles due to the language's distinct grammatical and emotional characteristics. This paper presents a robust methodology for classifying emotions in Marathi speech utilizing advanced signal processing, feature extraction, and machine learning techniques. The method entails collecting a diverse collection of Marathi speech samples and using pre-processing steps such as Pre-Emphasis and VAD to improve signal quality. Speech signals are segmented using the Hamming window to reduce discontinuities, and features such as MFCCs, pitch, intensity, and spectral properties are retrieved. For classification, an attentive DBN is paired with an SVM, which uses attention techniques and batch normalization to improve performance and reduce overfitting. The suggested approach surpasses existing models, with 98% accuracy, 98% F1-Score, 99% specificity, 99% sensitivity, 98% precision, and 98% recall.

Fusion Net-3: Denoising Based Secured Biometric Authentication Using Fingerprints

Sreemol R — 2025-08-15

Background

Fingerprint-based authentication is a critical biometric approach for ensuring security and accuracy. Traditional methods often face challenges such as noise and suboptimal feature extraction.

Methods

The proposed model, Fusion Net-3, addresses these issues through two phases: Enrollment and Authentication. In the Enrollment phase, hand images are scanned and pre-processed using improved bilateral filtering optimized by the Seagull Optimization Algorithm. Contrast enhancement is applied using Histogram Equalization. Features are extracted based on shape and texture, and optimal features are selected using the Falcon Inspired Jackal Optimization algorithm, which combines Golden Jackal and Falcon optimization techniques. These features are fused using the Geometric mean and Ronald Fisher score.

In the Authentication phase, similar pre-processing, feature extraction, and selection methods are applied. Secure data transmission is achieved through the blockchain technology. The Fusion Net-3 model, integrating CNN, ResNet-50, and U-Net, is used to detect efficient fingerprints.

Findings

The model achieved an accuracy of 98.95% and a Mean Squared Error (MSE) of 2.34% when implemented on a Python platform.

Results

The Fusion Net-3 model demonstrated superior performance compared to existing methods, effectively enhancing authentication accuracy and security.

Conclusion

The novel Fusion Net-3 model significantly improves fingerprint-based authentication systems by addressing noise and optimizing feature extraction and selection, ensuring high accuracy and security.

Enhanced Group Recommendation System: A Hybrid Context-Aware Approach with Collaborative Filtering for Location-Based Social Networks

Naimat Ullah Khan — 2025-08-15

In recent years, Location-based social networks (LBSNs) have gained significant popularity, enabling users to interact with points of interest (POIs) using modern technologies. As more and more people rely on LBSNs for finding interesting venues, contextually aware and relevant recommendation systems have become very beneficial with practical applications. In this re-search. We propose an enhanced hybrid recommendation system, designed for LBSNs to improve the accuracy of suggestions by integrating Collaborative Filtering (CF) methods with Singular Value Decomposition (SVD) to handle sparse data, along with context-aware modeling to tailor recommendations based on user interests, and group recommendation to accommodate multi-user scenarios. Additionally, we incorporate contextual aspects such as spatial proximity and temporal behavior into the model to ensure recommendations align closely with the user's present surroundings and their preferences. The proposed method extends further to group recommendations by considering individual inclinations into cohesive suggestions for groups interested in visiting POIs together. The proposed method is assessed using precision, recall, and F1 score, ensuring thorough evaluation of its performance. To further highlight context-aware recommendations, we use clustering based on user preference, temporal behavior, and category-wise interaction to identify patterns across various venue types. The proposed method shows improved recommendations, specifically based on data from LBSNs, and for developing an efficient solution for balanced user preferences with contextual influences.