Computer and Information Science
SkyViewSentinel: A Deep Learning-Driven Military Object Detection Application for Remote-Sensing Satellite Images
In today’s ever-changing world military forces face significant challenges in maintaining situational awareness and responding swiftly to emerging threats. Traditional aerial surveillance often fails to give timely and thorough intelligence over large areas. Limited coverage mistakes and difficulty noticing small changes on the ground hinder military operations. To address these problems this paper introduces the development of a deep learning-based web application named “SkyViewSentinel” a solution tailored specifically for military aerial surveillance.
The application framework i.e. SkyViewSentinel has been developed through multiple stages i.e. (i) pre-process the Xview overhead Satellite imagery dataset using Ground Truth refinement and image partitioning method (ii) employed a SOTA deep model i.e. YOLOv8 as a baseline architecture for the research problem and assessed the performance on experimental dataset (iii) a series of rigorous experiments have been conducted using deep model and obtained results are reported. (iv) Finally the trained model has been seamlessly integrated into the web application and develops a comprehensive web-based object detection application. The developed application detects military-based objects from real-time satellite images.
The developed application has shown promising results in identifying military objects from satellite images outperforming other contemporary methods. The designed framework has achieved an overall mAP score of 0.315 for all nine classes of military-based objects. For certain specific classes detection accuracy exceeds 70% demonstrating the robustness of the framework.
The designed web application enables users to detect military-based objects in the region provided by the user. By harnessing the power of satellite object recognition technology SkyViewSentinel provides a new way to monitor and understand activities in operational areas.
Deep Ensemble Learning using Transfer Learning for Food Classification
Deep learning models such as deep convolutional neural networks (CNNs) have undergone extensive scrutiny in the context of food classification because of their exceptional feature extraction capabilities.
Similarly ensemble-based learning approaches have exhibited great potential for achieving effective supervised classification.
We suggest an innovative approach to improve the effectiveness of deep learning-based food classification.
Our proposal involves a novel deep learning ensemble framework that draws inspiration from the fusion of deep learning models with ensemble learning based on random subspaces. The random subspaces play a role in diversifying the ensemble system in a straightforward but impactful way. Moreover to enhance the classification accuracy even more we explore transfer learning employing the migration of acquired weights from a single classifier to another (namely CNNs). This approach expedites the process of learning.
Results from experiments conducted using well-established food datasets illustrate that the suggested deep learning ensemble system delivers competitive performance compared to state-of-the-art techniques as evidenced by its classification accuracy.
The amalgamation of deep learning and ensemble learning holds substantial promise for dependable food categorization.
Image Encryption for Indoor Space Layout Planning
Indoor space layout planning and design involves sensitive and confidential information. To enhance the security and confidentiality of such data the study introduces an advanced image encryption algorithm. This algorithm is based on simultaneous chaotic systems and bit plane permutation diffusion aiming to provide a more secure and reliable approach to indoor space layout design.
The study proposes an image encryption algorithm that incorporates simultaneous chaotic systems and bit plane permutation diffusion. This algorithm is then applied to the process of indoor space layout planning and design. Comparative analysis is conducted to evaluate the performance of the proposed algorithm against other existing methods. Additionally a comparative testing of indoor space layout planning and design methods is carried out to assess the overall effectiveness of the research method.
Through the algorithm comparison test information entropy adjacent pixel distribution and response time were selected as evaluation indexes. The results demonstrated that the improved image encryption algorithm exhibited superior performance in terms of information entropy (with average information entropy of 7.9990) anti-noise attack capability (with PSNR value of 37.58db) and anti-differential attack capability (with NPCR and UACI values of 99.6% and 33.5%) when compared to the benchmark algorithm. In the actual application effect test the study selected space utilization functionality security ease of use confidentiality flexibility and other evaluation indicators. A comparative analysis of the actual application effects of various interior design projects revealed that the interior space layout planning and design method proposed in the study exhibited notable superiority over the comparison method across all indicators. In particular it showed overall advantages in space utilization (92.5% in modern apartment design) functionality score (9.5 in future living experience museum design) and safety assessment.
The above key results demonstrate that the improved image encryption algorithm and the designed indoor space layout planning method have substantial practical applications and are expected to enhance security and confidentiality in the field of indoor space layout planning thereby providing users with a more optimal experience.
Patent Selections
Acknowledgements to Reviewers
Advanced Digital Technologies for Promoting Indian Culture and Tourism through Cinema
Culture and Tourism are two mainly interrelated elements that contribute a lot to achieving Sustainable Development for any developing country especially India which has an extremely rich historical and cultural background. Tourism Industry is the fastest growing sector in a local economy creating several job opportunities which ultimately raise the standard of living of people which further raises the consumption level of goods and services resulting in a rise in the Gross Domestic Product (GDP) of a country. However various studies pointed out major promotional strategies concerning tourism and culture but an amalgamated promotional approach for both was still missing. With this motivation the current study aims at providing an amalgamated promotional approach in assimilation with the latest Industry 4.0 technologies such as Artificial Intelligence (AI) Machine Learning (ML) Big Data Blockchain Virtual Reality (VR) Digital Twin and Metaverse to the Indian tourism industry by reviewing prior research studies. The findings of the current study are establishing an online future travel demands forecasting system an online tourists’ destination personalized recommendation system an online tourist’s review analysis recommendation system and an online destination image recommendation system and provide the practical design for it through 1+5 Architectural Views Model and by applying several ML algorithms such as CNN BPNN SVM Collaborative Filtering K-means Clustering API Emotion and Naïve Bayes algorithms. Finally this study has discussed challenges and suggested vital recommendations for future work with the assimilation of Industry 4.0 technologies.
Artificial Intelligence for Cardiovascular Diseases
Globally cardiovascular disease (CVD) continues to be a major cause of death. Advancements in Artificial Intelligence (AI) in recent times present revolutionary opportunities for the diagnosis treatment and prevention of this condition. In this paper we review mainly the applications of AI in CVDs with its limitations and challenges. Artificial intelligence (AI) algorithms can quickly and precisely analyze medical images such as CT scans X-rays and ECGs helping with early and more accurate identification of a variety of CVD diseases. To identify those who are at a high risk of getting CVD AI models can also analyze patient data. This allows for early intervention and preventive measures. AI systems are also capable of analyzing complicated medical data to provide individualized therapy recommendations based on the requirements and traits of each patient. During patient meetings AI-powered solutions can also help healthcare practitioners by offering real-time insights and recommendations which may improve treatment outcomes. Machine learning (ML) which is a branch of AI and computer sciences has also been employed to uncover complex interactions among clinical variables leading to more accurate predictive models for major adverse cardiovascular events (MACE) like combining clinical data with stress test results has improved the detection of myocardial ischemia enhancing the ability to predict future cardiovascular outcomes. In this paper we will focus on the current AI applications in different CVDs. Also precision medicine and targeted therapy for these cardiovascular problems will be discussed.
A PSO-Optimized Neural Network and ABC Feature Selection Approach with eXplainable Artificial Intelligence (XAI) for Natural Disaster Prediction
“Artificial Intelligence will revolutionize our lives” is a phrase frequently echoed. The influence of Artificial Intelligence (AI) and Machine Learning (ML) extends across various aspects of our daily lives encompassing health education economics the environment and more.
A particularly formidable challenge lies in decision support especially in critical scenarios such as natural disaster management where artificial intelligence significantly amplifies its ongoing capacity to assist in making optimal decisions. In the realm of disaster management the primary focus often centers on preventing or mitigating the impact of disasters. Consequently it becomes imperative to anticipate their occurrence in terms of both time and location enabling the effective implementation of necessary strategies and measures. In our research we propose a disaster forecasting framework based on a Multi-Layer Perceptron (MLP) empowered by the Particle Swarm Optimization (PSO) algorithm. The PSO-MLP is further fortified by the incorporation of the Artificial Bee Colony (ABC) algorithm for feature selection pinpointing the most critical elements. Subsequently we employ the LIME (Local Interpretable Model-agnostic Explanations) model a component of eXplainable Artificial Intelligence (XAI). This comprehensive approach aims to assist managers and decision-makers in comprehending the factors influencing the determination of the occurrence of such disasters and increases the performance of the PSO-MLP model. The approach specifically applied to predict snow avalanches has yielded impressive results.
The obtained accuracy of 0.92 and an AUC of 0.94 demonstrate the effectiveness of the proposed framework. In comparison the prediction precision achieved through an SVM is 0.75 while the RF classifier yields 0.86 and XGBoost reaches 0.77. Notably the precision is further enhanced to 0.81 when utilizing XGBoost optimized by the grid-search.
These results highlight the superior performance of the proposed methodology showcasing its potential for accurate and reliable snow avalanche predictions compared to other established models.