Innovative Model Research Articles

An innovative approach for coordinating multiple sedated procedures in medically complex pediatric patients

ABSTRACT Children and youth with special health care needs often undergo a higher frequency of sedated procedures, increasing their risk for complications, prolonged hospitalizations, as well as increased time and cost burdens. By consolidating multiple procedures requiring anesthetic sedation, the risk and cost can be reduced for both families and health care systems. In this paper, we discuss an innovative model to coordinate procedures across internal and external providers to improve quality of care for this vulnerable patient population. Although preliminary, our findings suggest this approach may be beneficial to both the patient, family, and health system.

Research gaps in Intensive Comprehensive Aphasia Programs: A qualitative study

ABSTRACT Background Intensive Comprehensive Aphasia Programs (ICAPs) provide a variety of therapies for cohorts of individuals with aphasia. Converging evidence for proof of concept and acceptability data demonstrates that ICAPs improve patient outcomes; however, efforts to build the ICAP evidence base are vital to optimize its implementation and justify potential reimbursement. Aims This qualitative study characterizes evidence gaps in the ICAP literature base from the perspective of international aphasiologists who implement ICAPs. Methods Seven international ICAP program leaders participated in semi-structured interviews. A four-stage qualitative content analysis approach was applied by three trained independent raters to organize and elicit meaning from transcribed interviews. This qualitative study was approved by Idaho State University IRB (IRB-FY2021-270). Results Program access was a prominent theme with subthemes of treatment candidacy (participant characteristics; cohort makeup), program scalability, telehealth options, and stakeholder needs. The second theme was program structure with subthemes of optimizing treatment dose, standardizing outcome measures, refining ICAP comprehensive components, defining the wide array of delivery model applications, and sustainability considerations. Conclusions The ICAP evidence gap themes described in this study are integral to establishing a call to action for ICAP researchers and others who are fervent about intensive aphasia rehabilitation models. Building the ICAP evidence base will help to optimize implementation, justify potential reimbursement to insurers, contribute to the sustainability of this innovative service delivery model, and maximize rehabilitation options for individuals with aphasia.

Interpretable deep learning methods for multiview learning

BackgroundTechnological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries.ResultsWe propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) to learn nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and three real-world data for classification, clustering, and reconstruction tasks. For the classification tasks, iDeepViewLearn had competitive classification results with state-of-the-art methods in various settings. For the clustering task, we detected molecular clusters that differed in their 10-year survival rates for breast cancer. For the reconstruction task, we were able to reconstruct handwritten images using a few pixels while achieving competitive classification accuracy. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning.ConclusioniDeepViewLearn is an innovative deep learning model capable of capturing nonlinear relationships between data from multiple views while achieving feature selection. It is fully open source and is freely available at https://github.com/lasandrall/iDeepViewLearn.

Advancing disease identification in fava bean crops: A novel deep learning solution integrating YOLO-NAS for precise rust

A significant concern is the economic impact of agricultural diseases on the world’s crop production. The disease significantly reduces agricultural production across the world. Loss of nutrients caused by parasite infection of leaves, pods, and roots–the pathogenic agent that causes fava bean rust disease–decreases crop health. This work addresses this requirement by offering an innovative deep-learning model approach for early identification and classification of fava bean rust disease. The suggested method uses the effectiveness of modern YOLO-based object detection architectures like You Only Look Once –Neural Architecture Search (YOLO-NAS) L, YOLO-NASM, and YOLO-NASS, Faster Region-based Convolutional Neural Network (Faster R-CNN), and RetinaNet. An inclusive dataset of 3296 images of various lighting and background situations was selected for extensive model training. Each model underwent thorough training and adjusted parameters through careful experimentation. The models’ comparative studies found significant performance differences. The precision for YOLO-NASL was 82.10%; for YOLO-NASM, it was 84.80%; for YOLO-NASS, it was 83.90%; for Faster R-CNN, it was 75.51%; and for RetinaNet, it was 73.74%. According to the evaluation, model complexity and detection accuracy are directly correlated. YOLO-NASL, YOLO-NASM, and YOLO-NASS showed remarkable mean average precision values of 90.90%, 94.10%, and 92.60%, respectively, and became highly functional models. The fastest model was YOLO-NASS. Its satisfying recognition speed made real-time detection possible in particular applications. The YOLO-NASM model, which shows an extraordinary state-of-the-art performance, represents the pinnacle of our work. Its mean average precision (mAP@0.5) was 94.10%, with notable values of 90.84%, 96.96%, and 84.80% for the F1-score, Recall, and precision, respectively. This investigation addresses a critical need in agricultural disease management, aligning with broader global efforts toward sustainable agriculture. Our studies add to the knowledge about precision agriculture and inspire practical, long-lasting disease management techniques in the agricultural industry. The real-time performance of the system will need to be improved, and satellite imagery integration may be considered in the future to provide more comprehensive coverage.

Performance evaluation in teaching: Dissecting student evaluations in higher education

Numerous studies have highlighted the significant role of Student Evaluations of Teaching (SETs) as a key metric for assessing teaching quality in Higher Education (HE). Building upon these insights, our study introduces an innovative four-tiered model, derived from diverse research, to examine the reliability of SETs. This model addresses biases associated with SETs, delving into both statistical anomalies and cognitive biases, with particular emphasis on often-overlooked hidden context and timing factors. We reveal that these biases can distort SET scores, leading to potentially inaccurate representations of both individual and comparative academic performances. The implications of our research are significant for those influencing HE policy-making and performance evaluation. We echo previous calls for a more expansive approach to teaching effectiveness, essential for genuine insight into teaching quality. By adopting this perspective, HE can design better-informed strategies, ensuring policies and practices reflect the diverse nature of teaching excellence.

Hybrid Crow Search and RBFNN: A Novel Approach to Medical Data Classification

The Radial Basis Function Neural Network (RBFNN) is frequently employed in artificial neural networks for diverse classification tasks, yet it encounters certain limitations, including issues related to network latency and local minima. To tackle these challenges, researchers have explored various algorithms to enhance learning performance and alleviate local minima problems. This study introduces a novel approach that integrates the Crow Search Algorithm (CSA) with RBFNN to augment the learning process and address the local minima issue associated with RBFNN. The study evaluates the performance of this innovative model by comparing it to state-of-the-art models like Flower-pollination-RBNN (FP-NN), Artificial Neural Network (ANN), and the conventional RBFNN. To assess the efficacy of the proposed model, the study employs specific datasets, such as the Breast Cancer and Thyroid Disease datasets from the UCI Machine Repository. The simulation results illustrate that the proposed model surpasses other models in terms of accuracy, exhibiting lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. Specifically, for the Breast Cancer dataset, the proposed model attains an accuracy of 99.9693%, MSE of 0.000307024, and MAE of 0.00789449. Likewise, for the Thyroid Disease dataset, the proposed model achieves an accuracy of 99.9535%, along with MSE of 0.000464932 and MAE of 0.0057098. For the diabetes dataset, the proposed model demonstrates an accuracy of 98.8073%, MSE of 0.003024, and MAE of 0.009449. In summary, this analysis underscores the enhanced accuracy and effectiveness of the proposed model when compared to traditional approaches.

PulmonU-Net: a semantic lung disease segmentation model leveraging the benefit of multiscale feature concatenation and leaky ReLU

Pulmonary diseases impact lung functionality and can cause health complications. X-ray imaging is an initial diagnostic approach for evaluating lung conditions. Manual segmentation of lung infections from X-rays is time-consuming and subjective. Automated segmentation has gained interest to reduce clinician workload. Semantic segmentation involves labelling individual pixels in X-rays to highlight infected regions. This article presents PulmonU-Net, an innovative semantic segmentation model using PulmonNet modules as the base network to highlight infected areas in chest X-rays. PulmonNet modules leverage global and local chest X-ray characteristics to create intricate feature maps. Incorporating leaky ReLU activation enables uninterrupted neuron functioning during learning. By adding PulmonNet modules in the encoder's deeper layers, the model addresses vanishing gradients and improves dice similarity coefficient to 94.25%. Real-time testing and prediction visualization demonstrate PulmonU-Net's effectiveness for automated lung infection segmentation from chest X-rays.

The Cascadia Coastal Hazards and Resilience Training, Education, and Research (CHARTER) Fellows Mentoring Model for Underrepresented Students

Engaging underrepresented scholars in climate change-related research is not just a matter of diversity and inclusion but a necessity for addressing the complex challenges we face as a global society. While government officials, scientists, environmentalists, practitioners, and other stakeholders have increased calls for collaborative approaches, historically underrepresented communities, specifically students, still need to be included in such initiatives and collaborations. The National Science Foundation (NSF)-funded Coastal Hazards and Resilience Training, Education, and Research (CHARTER) program responds to this gap by explicitly incorporating and supporting historically underrepresented students from K-12, community college, undergraduate, and graduate schools with diverse lived and educational backgrounds into disaster and hazard research and spaces. Through the CHARTER Fellows program, we maintain that our model:1. Assists in combating the barriers that historically marginalized and underrepresented students face by creating a pipeline training cohort system.2. Increases representation in the disaster and hazards field by centering students that reflect the overall population.3. Advances disaster science by including diverse perspectives and creating convergent work among other researchers, practitioners, and educators.4. Creates practical opportunities to produce equitable experiences and educational outcomes for underrepresented groups.This project highlights our experience developing and facilitating this new innovative minority mentoring model. Through this work, we provide program details, lessons learned, and recommendations for faculty to incorporate our model when engaging with underrepresented undergraduate students.

Performance of multistage-fractured horizontal wells with secondary discrete fractures in heterogeneous tight reservoirs

AbstractA significant portion of tight sandstone reservoirs commonly displays intricate fluvial channels or fault systems. Despite various attempts at analytical/semi-analytical modeling of multistage-fractured horizontal wells (MFHWs) in unconventional reservoirs, the majority of studies have focused on scenarios with homogeneous original physical properties, neglecting cases where MFHWs traverse multiple regions in channelized heterogeneous reservoirs. Comprehending the influence of heterogeneous and leaky faults on the performance of MFHWs is essential for efficient development. This study presents an innovative semi-analytical model to analyze the pressure transient behavior of MFHWs with secondary fractures as they traverse multiple regions in banded channel heterogeneous reservoirs, particularly considering the presence of partially-communicating faults. The approach combines the source method and Green’s function method to obtain solutions, introducing a novel technique for discretizing fractures without discretizing interfaces. The effects of the reservoir heterogeneity, partially-communicating faults and fractures system on pressure behavior are analyzed. The results indicate that the pressure behavior of MFHWs passing through regions with different physical properties exhibits distinctive characteristics, differing from both the homogeneous case and the heterogeneous cases where the well does not traverse distinct regions. Permeability heterogeneity influences the curves of all other flow regimes, except the early and late flow regimes. Faults affect transient pressure behavior only when not positioned in the middle of each two primary fractures. Region area heterogeneity primarily influences the medium flow regimes. This work provides valuable insights into the performance of MFHWs in channelized heterogeneous reservoirs, offering technical support for well testing in these reservoirs.

ADVANCING PHILANTHROPY FOR OPHTHALMOLOGY IN INDONESIA: UNLEASHING THE POWER OF GIVING FOR VISION CARE

Health philanthropy plays a crucial role in supporting charitable activities in the field of ophthalmology. The Indonesian Ophthalmologists Association (PERDAMI) has been actively engaged in various social initiatives to improve eye care services. However, it is imperative to consider long-term sustainability in funding to ensure the continuity of philanthropic efforts. Additionally, the recent regulatory changes in Indonesia highlight the need for philanthropic practices to comply with legal frameworks. This study presents the key highlights of a social-entrepreneurship session presented at the 48th Annual Scientific Meeting of the PERDAMI in August 2023, with aim to emphasize the importance of collaboration among stakeholders involved in ophthalmic philanthropy, including professionals, institutions, and grantors. The future sustainability of vision care is vital, and it requires the exploration of innovative funding models, the promotion of public-private partnerships, and the cultivation of a culture of giving. By raising awareness of this issue and incorporating it into strategic planning, stakeholders can work towards ensuring the availability and accessibility of quality eye care services in Indonesia. In conclusion, this study highlights the importance of integrating collaboration, needs assessment, good governance, and sustainability considerations in health philanthropy practices within Indonesian ophthalmology. These efforts will help shape the future of vision care and empower the PERDAMI to deliver meaningful and lasting impact in eye health.

Patient-derived tumor spheroid-induced angiogenesis preclinical platform for exploring therapeutic vulnerabilities in cancer

This study addresses the demand for research models that can support patient-treatment decisions and clarify the complexities of a tumor microenvironment by developing an advanced non-animal preclinical cancer model. Based on patient-derived tumor spheroids (PDTS), the proposed model reconstructs the tumor microenvironment with emphasis on tumor spheroid-driven angiogenesis. The resulting microfluidic chip system mirrors angiogenic responses elicited by PDTS, recapitulating patient-specific tumor conditions and providing robust, easily quantifiable outcomes. Vascularized PDTS exhibited marked angiogenesis and tumor proliferation on the microfluidic chip. Furthermore, a drug that targets the vascular endothelial growth factor receptor 2 (VEGFR2, ramucirumab) was deployed, which effectively inhibited angiogenesis and impeded tumor invasion. This innovative preclinical model was used for investigating distinct responses for various drug combinations, encompassing HER2 inhibitors and angiogenesis inhibitors, within the context of PDTS. This integrated platform could potentially advance precision medicine by harmonizing diverse data points within the tumor microenvironment with a focus on the interplay between cancer and the vascular system.

Cultural Heritage for Sustainable Education Amidst Digitalisation

The integration of cultural heritage in education facilitates critical thinking, experiential learning, cross-cultural collaborative learning and ultimately, quality learning experiences. This process is further enhanced by the increasing adoption of digital technology, which makes education more accessible. However, some countries in the European Union have low digital literacy and a high student dropout rate. Also, the use of cultural heritage in education is declining as young learners are becoming increasingly unaware of their cultural identity. Within this framework, a study of mixed methods (questionnaires and interviews) was conducted in three European countries to examine digital and cultural heritage competencies among young learners. The results of the paper reveal how digital cultural heritage increases learners’ resilience by promoting competences for digital transformation, which in turn enhances learning and engagement with cultural heritage. Drawing on our findings, the paper proposes a new innovative hybrid model within the framework of sustainable education (SE).

An innovative model for capturing seasonal patterns of train passenger movement using exogenous variables and fuzzy time series hybridization

Train is a popular mode of ground transportation due to the ability to accommodate a large number of passenger, save time, avoid traffic congestion, offer cost-effective fares, and provide a relatively high level of safety. These benefits contribute to an annual increase in passenger numbers, particularly during holidays and the year-end period. Consequently, it is essential for management to anticipate potential capacity constraints faced by train operators. Detecting this challenge encompasses observing train passenger count trends at the end of each year, which can be effectively analyzed using Seasonal Autoregressive Integrated Moving Average (SARIMA) model to account for seasonal effects. Train passenger surges also align with the Eid Al-Fitr holiday in Indonesia, an event that varies annually according to the Hijri calendar. To address this issue, SARIMA was adapted to include exogenous effects in the form of calendar variations, producing Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX). Furthermore, a novel numerical model was proposed using Fuzzy Time Series Markov Chain (FTSMC). Consequently, it is vital for management to anticipate potential capacity constraints that train operators might face. This innovation was introduced through hybrid SARIMA-FTSMC and hybrid SARIMAX-FTSMC. The results showed that hybrid SARIMA-FTSMC delivered the highest accuracy level with the smallest error value, providing more precise insights into train passenger movement patterns. These modeling results also offered valuable recommendations for risk management to address capacity limitations during the Eid Al-Fitr holiday and the year-end period, enabling train operators to optimize the services effectively.

Proposing information and communication Technology (ICT)-Based Learning transformation to create competitive human resources: A theoretical review

To address the challenges posed by the digital age and worldwide competitiveness, learning transformation that focuses on Information and Communication Technology (ICT) has been identified as a key solution for forming competitive human resources in the 21st century. This research employed literature analysis methods by referring to relevant recent research taken from various well-known scientific journal databases. Data analysis aimed to reveal trends and best practices in implementing innovative 21st-century learning models, utilizing ICT-based learning media, and preparing human resources to maximize the potential of Massive Open Online Courses (MOOCs). This study produces strategic recommendations and guidelines for educational institutions to implement innovative 21st-century learning models, utilize ICT-based learning technology, and prepare human resources to implement MOOCs optimally. This endeavor aims to equip graduates with globally relevant skills. The transformation of ICT-based learning and the use of MOOCs is recognized as an essential step to develop human resources capable of global competition in the job markets. This research indicates positive impacts to respond current global challenges and nurturing the required competencies leading to a better employability for graduates students working in the digital era with global industries.

An innovative Indigenous-led model for integrated COVID-19 case management in Auckland, New Zealand: lessons from implementation.

In Aotearoa/New Zealand (NZ), the Indigenous Māori population have been more severely impacted than non-Māori throughout the COVID-19 pandemic, and less well served by NZ's COVID-19 response. This case-study describes an innovative Indigenous-led service delivery model, which was designed and implemented to improve the case and contact management of Māori with COVID-19 in Auckland. We outline the context in which the conventional public health case and contact management was failing Māori and the factors which enabled Indigenous innovation and leadership. We describe the details of the model and how the approach fundamentally differed to the conventional approach to care. Qualitative and quantitative data on impact of the model are shared, along with the key barriers and enablers in the implementation of the model. The Māori Regional Coordination Hub (MRCH) model offers a valuable alternative to the conventional public health case and contact management approach, and this case study highlights lessons which may be applicable to improving the design and delivery of public health services to other Indigenous and marginalized groups.

METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF THE AIR DEFENSE SYSTEM

This academic article introduces a pioneering methodology for the comprehensive assessment of air defense systems, addressing existing shortcomings in evaluation approaches. The subject of this study revolves around the development and implementation of an advanced methodology for assessing air defense systems. еThe primary aim is to rectify existing evaluation shortcomings by introducing a holistic model that significantly enhances both efficiency and effectiveness in the evaluation of air defense systems. This methodology incorporates critical elements such as system description, mission, objectives, combat environment, threat, and concept. By explicitly considering factors like system flexibility, survivability, and operational concepts, the study surpasses traditional evaluations, providing a nuanced understanding of the capabilities inherent in air defense systems. The methodology places particular emphasis on the importance of threat analysis, addressing uncertainties related to enemy forces and tactics. Additionally, the study introduces an innovative evaluation model employing tactical scenarios to assess system reliability, availability, and durability, integrating combat environment factors and potential adversary combat options. The research contributes to the academic discourse by providing a systematic and thorough approach to air defense system evaluation, tailored to the complexities of modern warfare and in alignment with the evolving military and technological landscapes. The article suggests avenues for future research to delve into nuanced criteria selection and aggregation methods, aiming to further refine the proposed evaluation methodology.

Local Wisdom-Based Policy Innovation in Indonesia During 2018-2021

Several studies on the innovation of Indonesian public services only provide general and less specific descriptions of inclusive public services based on local wisdom. This study aims to find innovative models of local wisdom-based public services in Indonesia. This study is a descriptive-exploratory study, and the research method is an archival one emphasizing secondary data use. The findings revealed that (1) Local government dominated policy innovation proposals based on local wisdom compared to the central government because of the proximity aspect of service distance to objects, so the type of service is specific. 2) Product-oriented service types dominated service innovation due to specific, urgent, and critical service needs that must be resolved immediately. 3) Innovation with the outcome of problem-solving was very dominant because it is related to critical service issues that require solving problems by minimizing existing local cultural clashes. 4) The sectors of education, community empowerment, food security, environmental preservation, and the health sector almost evenly influenced service policy innovations based on local wisdom, because it is a wedge between direct contact with basic community services and the culture adopted by local communities. 5) Innovator originating from Java and Outside Java had competed quite dynamically over the last four years, because Outside Java excelled in the quantity of both organizations and local communities, while Java excelled in its human resources and infrastructure's quality.

Inter-relationships between cardiovascular, renal and metabolic diseases: Underlying evidence and implications for integrated interdisciplinary care and management.

Cardiovascular, renal and metabolic (CaReMe) diseases are individually among the leading global causes of death, and each is associated with substantial morbidity and mortality. However, as these conditions commonly coexist in the same patient, the individual risk of mortality and morbidity is further compounded, leading to a considerable healthcare burden. A number of pathophysiological pathways are common to diseases of the CaReMe spectrum, including neurohormonal dysfunction, visceral adiposity and insulin resistance, oxidative stress and systemic inflammation. Because of the shared pathology and common co-occurrence of the CaReMe diseases, the value of managing these conditions holistically is increasingly being realized. A number of pharmacological and non-pharmacological approaches have been shown to offer simultaneous metabolic, cardioprotective and renoprotective benefits, leading to improved patient outcomes across the CaReMe spectrum. In addition, increasing value is being placed on interdisciplinary team-based and coordinated care models built on greater integration between specialties to increase the rate of early diagnosis and adherence to practice guidelines, and improve clinical outcomes. This interdisciplinary approach also facilitates integration between primary and specialty care, improving the patient experience, optimizing resources, and leading to efficiencies and cost savings. As the burden of CaReMe diseases continues to increase, implementation of innovative and integrated care delivery models will be essential to achieve effective and efficient chronic disease management and to ensure that patients benefit from the best care available across all three disciplines.

Numeric Validation of the Inversion Model of Electrical Resistivity Imaging Method using the Levenberg-Marquardt Algorithm

This paper introduces a new application of the Electrical Resistivity Imaging (ERI) method within the realm of structural assessment, deviating from its conventional use in geology. The study presents an innovative inversion model that incorporates the Levenberg-Marquardt algorithm, representing a notable leap in seamlessly integrating ERI into structural analysis. Rigorous validation of the inversion methodology is conducted through extensive benchmarking against simulated reference data, focusing on 1D and 2D resistivity distributions within timber specimens. By utilizing known resistivity fields, the paper quantitatively validates the accuracy of reconstructed models obtained through numerical simulations. Notably, both longitudinal and transverse surveys exhibit exceptional outcomes, showcasing a high correlation with the actual resistivity profiles, achieved within a concise 10-13 iterations. This meticulous validation process conclusively underscores the effectiveness and precision of the proposed inversion approach. Beyond its scientific contribution, this research expands the conventional boundaries of ERI application and establishes it as an invaluable tool for structural monitoring.

IFGAN—A Novel Image Fusion Model to Fuse 3D Point Cloud Sensory Data

To enhance the level of autonomy in driving, it is crucial to ensure optimal execution of critical maneuvers in all situations. However, numerous accidents involving autonomous vehicles (AVs) developed by major automobile manufacturers in recent years have been attributed to poor decision making caused by insufficient perception of environmental information. AVs employ diverse sensors in today’s technology-driven settings to gather this information. However, due to technical and natural factors, the data collected by these sensors may be incomplete or ambiguous, leading to misinterpretation by AVs and resulting in fatal accidents. Furthermore, environmental information obtained from multiple sources in the vehicular environment often exhibits multimodal characteristics. To address this limitation, effective preprocessing of raw sensory data becomes essential, involving two crucial tasks: data cleaning and data fusion. In this context, we propose a comprehensive data fusion engine that categorizes various sensory data formats and appropriately merges them to enhance accuracy. Specifically, we suggest a general framework to combine audio, visual, and textual data, building upon our previous research on an innovative hybrid image fusion model that fused multispectral image data. However, this previous model faced challenges when fusing 3D point cloud data and handling large volumes of sensory data. To overcome these challenges, our study introduces a novel image fusion model called Image Fusion Generative Adversarial Network (IFGAN), which incorporates a multi-scale attention mechanism into both the generator and discriminator of a Generative Adversarial Network (GAN). The primary objective of image fusion is to merge complementary data from various perspectives of the same scene to enhance the clarity and detail of the final image. The multi-scale attention mechanism serves two purposes: the first, capturing comprehensive spatial information to enable the generator to focus on foreground and background target information in the sensory data, and the second, constraining the discriminator to concentrate on attention regions rather than the entire input image. Furthermore, the proposed model integrates the color information retention concept from the previously proposed image fusion model. Furthermore, we propose simple and efficient models for extracting salient image features. We evaluate the proposed models using various standard metrics and compare them with existing popular models. The results demonstrate that our proposed image fusion model outperforms the other models in terms of performance.