Computer 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.
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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.
Revolutionizing Agriculture: A Comprehensive Review of IoT Farming Technologies
IoT technology has triggered a revolutionary transformation across various industries with agriculture being no exception. Smart farming the integration of IoT in farming has led to a complete overhaul of traditional agricultural practices by seamlessly combining sensor networks data analytics and automation. This comprehensive review aims to explore the diverse contributions of IoT in agriculture synthesising insights from a wide range of research papers. The analysis delves into the multifaceted applications of IoT in farming assessing its profound impact on productivity resource management environmental sustainability and the challenges faced during implementation. By merging advanced sensor networks with data analytics IoT in agriculture has given rise to intelligent farming practices empowering farmers to make data-driven decisions and optimize their operations. Utilizing sensors to monitor soil moisture temperature and nutrient levels along with advanced analytics allows farmers to make real-time adjustments thus maximizing crop yield and quality. Resource management has also been greatly affected by IoT in agriculture. The adoption of precision agriculture techniques enables farmers to precisely administer water fertilizers and pesticides minimizing wastage and reducing the environmental footprint of conventional agricultural practices. Efficient resource use enhances agricultural sustainability and contributes to cost reduction and increased profitability for farmers. Moreover the integration of IoT technology in agriculture holds great promise for fostering environmental sustainability. Farmers can proactively detect early signs of pest infestations or diseases by deploying IoT-based monitoring systems facilitating timely intervention and reducing the need for excessive chemical treatments. This environmentally friendly approach helps preserve biodiversity minimize soil and water pollution and promote eco-conscious agricultural practices. Despite the numerous advantages IoT implementation in agriculture does pose particular challenges. Connectivity issues data security and privacy concerns and the initial high costs of IoT deployment are among the primary obstacles faced by farmers. Addressing these challenges requires collaboration among stakeholders including researchers policymakers and technology providers to develop sustainable solutions that can facilitate the broader adoption of IoT in agriculture. In conclusion this review paper sheds light on the immense potential of IoT technology in transforming the agricultural landscape. Smart farming through IoT integration paves the way toward sustainable food production increased productivity and efficient resource management. However overcoming the challenges and ensuring seamless IoT integration is vital to fully harnessing this groundbreaking technology's benefits in agriculture.
A Study on Privacy Preserving Techniques in Fog Computing: Issues, Challenges, and Solutions
Privacy plays a substantial role in both public and private databases especially in the healthcare industry and government sectors require a high-confidential data transmission process. Most often these data contain personal information that must be concealed throughout data processing and transmission between terminal devices and cloud data centers such as username ID account information and a few more sensitive details. Recently fog computing highly utilized for such data transmission storing and network interconnection processes due to its low latency mobility reduced computational cost position awareness data localization and geographical distribution. It delivers to cloud computing and the widespread positioning of IoT applications. Since fog-based service is provided to the massive-scale end-end users by fog server/node privacy is a foremost concern for fog computing. Fog computing poses several challenges if it comes to delivering protected data transfer; making the development of privacy preservation strategies particularly desirable. This paper exhibits a systematical literature review (SLR) on privacy preservation methods developed for fog computing in terms of issues challenges and various solutions. The main objective of this paper is to categorize the existing privacy-related research methods and solutions that have been published between 2012 and 2022 using analytical and statistical methods. The next step is to present specific practical issues in this area. Depending on the issues the merits and drawbacks of each suggested fog security method are explored and some suggestions are made for how to tackle the privacy concerns with fog computing. To build deploy and maintain fog systems several imminent motivational directions and open concerns in this topic were presented.
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Real Time Object Detection Algorithm in Foggy Weather based on WVIT-YOLO Model
To address the challenges of low visibility object recognition difficulties and low detection accuracy in foggy weather this paper introduces the WVIT-YOLO real-time fog detection model built on the YOLOv5 framework. The NVIT-Net backbone network incorporating NTB and NCB modules enhances the model's ability to extract both global and local features from images.
An efficient convolutional C3_DSConv module is designed and integrated with channel attention mechanisms and ShuffleAttention at each upsampling stage improving the model's computational speed and its ability to detect small and blurry objects. The Wise-IOU loss function is utilized during the prediction stage to enhance the model's convergence efficiency.
Experimental results on the publicly available RTTS dataset for vehicle detection in foggy conditions demonstrate that the WVIT-YOLO model achieves a 3.2% increase in precision a 9.5% rise in recall and an 8.6% improvement in mAP50 compared to the baseline model. Furthermore WVIT-YOLO shows a 9.5% and 8.6% mAP50 improvement over YOLOv7 and YOLOv8 respectively. For detecting small and blurry objects in foggy conditions the model demonstrates approximately a 5% improvement over the benchmark significantly enhancing the detection network's generalization ability under foggy conditions.
This advancement is crucial for improving vehicle safety in such weather. The code is available at https://github.com/QinghuaZhang1/mode.
Ransomware Mitigation for Secure and Smart Cloud Manufacturing Using GeniLeaf Decision Tree with Load Balancing
Cyber-attacks related to ransomware have increased significantly in cloud manufacturing industries over the last decade causing considerable disruptions to organizations. This type of information may also include personal details patent rights bank account details etc. This type of malware requires new and better mitigation methods.
The primary objective of this study was to provide an algorithm to execute experiments using genetic algorithms for load balancing with decision trees and mitigate ransomware.
Hybrid analysis and machine learning techniques were used in this study to identify ransomware. Since a wide range of samples impacted by ransomware share most of the characteristics it may be possible to use this study to detect current and future malware variants in industries.
In patented industrial technology ransomware mitigation plays a crucial role based on the analysis of various papers and patents-to-science references. In this study a machine learning mitigation algorithm GeniLeaf Decision Tree (GLDT) was applied to a featured dataset using a genetic algorithm with a decision tree.
Machine learning and load balancing are used to gain insight into ransomware behavior. By using the proposed approach to mitigate ransomware and spoofing patterns a high level of accuracy is achieved. Using GeniLeaf Decision Trees to mitigate ransomware is a significant innovation.
Fourth Industrial Revolution (4IR)-based Technology and Practices
Predicting Customer Retention and Online Shopping Perception: Highly Recommended Machine Learning Approach
In today’s fast-paced world online shopping has become a staple activity especially for busy individuals seeking convenience. Online shopping has emerged as a predominant activity particularly popular among the younger demographic. The hectic lifestyles of the working class have elevated online shopping to a convenient and often essential practice providing a respite amidst their busy schedules. Simultaneously businesses in the competitive E-commerce market recognize the importance of understanding customer behavior and perceptions to foster loyalty and ensure sustainability.
In response to the evolving landscape of online shopping we have undertaken a comprehensive analysis using machine learning techniques. Our approach involves the utilization of machine learning algorithms to recognize patterns and make precise predictions. We divide the data set into quarters assess sales income per quarter and further partition the data into training and testing sets. The subsequent steps involve forecasting revenue for upcoming quarters and identifying top-performing commodities to devise a Python-based model for strategic customer retention. The methodology begins by segregating the sales data set into quarters followed by the calculation of quarterly sales income. Using a machine learning system our approach forecasts revenue for future quarters and identifies high-performing commodities based on quarterly sales rates.
The culmination of these results leads to the development of a Python-based model for strategic customer retention. The outcome of our analysis not only facilitates precise predictions of future revenue but also contributes to the creation of a strategic model for customer retention.
By identifying high-performing commodities and leveraging machine learning algorithms we establish a symbiotic loop of sales and purchasing between customers and the E-commerce company. This algorithmically-driven loop promises incremental profitability for both the customers and the E-commerce company creating a symbiotic relationship.
Biofuels Policy as the Indian Strategy to Achieve the 2030 Sustainable Development Goal 7: Targets, Progress, and Barriers
The use of 20% blended biofuels to fossil fuels is one of the important targets of the Government of India to address the impacts of Climate Change energy-related environmental pollution and illnesses due to air pollution.
The National Policy on Biofuels 2018 (NPB 2018) is in place to boost the emerging production of biofuels and therefore respond to different international agreements including the Sustainable Development Goals (SDGs) and the Paris Agreement. Hence this article examined the production of biofuels in India in line with Agenda 2030 to project the share to be taken by biofuels as its contribution to the country’s energy needs.
The results were compromising; it was observed that the data from 2000 up to 2017 were not on the side of realizing the targets of production and consumption of biofuels in India whereas the data from 2018 up to now showed a hope of achieving 2030 set goal of E20 petrol in 2025-26 and E5 diesel in 2030. It was clear that the production of bioethanol was boosting compared to its sibling biodiesel and renewable energy will continue to have a hard take a good share in the total annual energy used in India.
It is recommended to share data between different stakeholders to promote more research as the low performance in achieving the targets was due to poor communication and missing technology rather than the lack of feedstock or unavailability of production facilities.
Transient Empirical Machine Learning Models based on Bit Coin Price Prediction using High and Low Values
Cryptocurrency is the new mode of transaction that is slowly replacing the conventional assets of transactions. With the rise and advancement in the technology called blockchain and the volatile nature of the market cryptocurrencies are showing possibilities of making huge profits. This new trend is slowly becoming accessible to all classes of society in every part of the world while creating a great opportunity for scholars and analysts to conduct new research. However unlike the stock market the prices of cryptocurrencies show highly volatile and dynamic behaviour.
Several research in the past have been performed to predict the nature and other aspects of cryptocurrencies with the aid of machine learning to forecast the future market based on past data. In the proposed work 6 different machine-learning models have been used for forecasting the prices of cryptocurrencies. The algorithms used are Autoregressive Integrated Moving Average (ARIMA) Gated Regression Unit (GRU) Long Short-Term Memory (LSTM) Bi-LSTM and Vector Autoregression (VAR).
Further the accuracies of different models are compared and analysed. Furthermore these models are used to predict the first-order difference between 24 hours of all-time high and low values. This evaluation gives us an idea of where the price band of the currency and where is it most likely to be present during any 24-hour timespan.
This can effectively lead to better decision making in choosing crypto-currency. The GRU model achieved the best performance in cryptocurrency prediction with an RMSE of 0.2242 and MAE of 0.1598 for BTC prices while the LSTM followed closely with an RMSE of 0.3473 and MAE of 0.2557. Both models outperformed ARIMA Bi-LSTM VAR and FB Prophet which showed significantly higher error rates.