Computer and Information Science
Data Security and Privacy Preservation in Cloud-Based IoT Technologies: An Analysis of Risks and the Creation of Robust Countermeasures
The Internet of Things (IoT) is a revolutionary technology being used in many different industries to improve productivity automation and comfort of the user in the cloud and distributed computing settings. Cloud computing is essential because it makes data management and storage more effective by automatically storing and examining the enormous amounts of data generated by Internet of Things applications. End users companies and government data are consequently migrating to the cloud at an increasing rate. A survey of the literature however reveals a variety of issues including data integrity confidentiality authentication and threat identification that must be resolved to improve data security and privacy. To effectively address contemporary data security concerns the existing approaches need to be improved. Ensuring secure end-to-end data transmission in a cloud-IoT situation requires innovative and dependable protocol architecture. New technologies that address some of the issues related to cloud data include edge computing fog blockchain and machine learning. This paper provides a thorough examination of security risks classifying them and suggesting possible defenses to safeguard cloud-IoT data. It also highlights innovative approaches such as blockchain technology and machine learning applied to privacy and data security. The paper also explores existing issues with respect to data privacy and security in today's cloud-IoT environments. It suggests possible future directions including the need for end-user authentication enhanced security and procedures for recovering data in the event of an attack.
A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs
New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs such as data confidentiality and message access control still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs including the protection of data storage user authentication access control system availability across multiple network connections and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.
The Inverse-Consistent Deformable MRI Registration Method Based on the Improved UNet Model and Similarity Attention
Deformable image registration is an essential task in medical image analysis. The UNet model or the model with the U-shaped structure has been popularly proposed in deep learning-based registration methods. However they easily lose the important similarity information in the up-sampling stage and these methods usually ignore the inherent inverse consistency of the transformation between a pair of images. Furthermore the traditional smoothing constraints used in the existing methods can only partially ensure the folding of the deformation field.
An inverse consistent deformable medical image registration network (ICSANet) based on the inverse consistency constraint and the similarity-based local attention is developed. A new UNet network is constructed by introducing similarity-based local attention to focus on the spatial correspondence in the high-similarity space. A novel inverse consistency constraint is proposed and the objective function of the new form is presented with the combination of the traditional constraint conditions.
The performance of the proposed method is compared with the typical registration models such as the VoxelMorph PVT nnFormer and TransMorph-diff model on the brain IXI and OASIS datasets.
Experimental results on the brain MRI datasets show that the images can be deformed symmetrically until two distorted images are well matched. The quantitative comparison and visual analysis indicate that the proposed method performs better and the Dice index can be improved by at least 12% with only 10% parameters.
This paper presents a new medical image registration network ICSANet. By introducing a similarity attention gate it accurately captures high-similarity spatial correspondences between source and target images resulting in better registration performance.
GWTBFO, Revolutionizing Cloud Efficiency: A Novel Approach Using Grey Wolf-Based TOPSIS and Bacterial Foraging Firefly Optimization
The growing demand for cloud computing services necessitates innovative strategies to enhance cloud deployment efficiency. Existing cloud load balancing models often grapple with suboptimal resource allocation leading to increased task makespan lowered virtual machine (VM) efficiency and failure to meet task deadlines effectively.
Addressing these limitations this study aims to introduce a novel model that significantly improves cloud deployment efficiency through a strategic blend of pre-emptive load analysis and resource pre-allocation operations.
The proposed model hinges on the innovative use of Grey Wolf-based TOPSIS (GWTOPSIS) operations for the initial segregation of VMs. This approach considers VM capacity current load and availability levels dynamically modifying internal weights to adapt VM categories based on fluctuating demand. The novelty of GWTOPSIS lies in its extension of traditional TOPSIS methods allowing for more responsive and demand-aligned VM categorization. Furthermore the clustering of new tasks based on makespan deadline and resource utilization levels-with a higher preference for makespan-addresses critical task scheduling challenges. A pivotal aspect of our model is the integration of the Bacterial Foraging Firefly Optimizer (BFFO) for task assignment to VMs. This optimizer synergizes the exploratory prowess of bacterial foraging with the efficient search capabilities of fireflies leading to a more effective task-to-VM assignment process.
Empirical evaluations of our model reveal substantial improvements over existing models: a reduction in task makespan by 8.5% a 3.9% boost in VM computation efficiency a 1.9% enhancement in deadline hit ratio a 3.5% increase in task diversity a 2.4% improvement in execution efficiency and a 3.5% reduction in decision delay.
These improvements not only demonstrate the efficacy of our model in real-time cloud deployment scenarios but also underscore its potential to revolutionize cloud resource management.
Analysis and Classification of Medical Images Using Deep Learning Algorithms
Nowadays Artificial intelligence and machine learning have emerged as a powerful tool for the analysis of medical images such as MRI scans. This technology holds significant potential to improve diagnostic services and accelerate medical advances by facilitating clinical decision-making.
In this work we developed a Convolutional Neural Network (CNN) model specifically designed for the classification of medical images. Using a selected database the model achieved a classification accuracy of 92%. To further improve the performance we leveraged the pre-trained VGG16 model which increased the classification accuracy to 100%. Additionally we preprocessed the MRI images using the Roboflow platform and then developed YOLOv5 models for the detection of tumors infections and cancerous lesions.
The results demonstrate a localization accuracy of 50.41% for these medical conditions.
This research highlights the value of AI-driven approaches in enhancing medical image analysis and their potential to support more accurate diagnoses and accelerate advancements in healthcare.
Basal Cell Carcinoma Detection Using Convolutional Neural Network
Basal Cell Carcinoma (BCC) is the most prevalent type of skin cancer accounting for three-quarters of all cancer cases. It is often confused with other benign lesions such as Acne Vulgaris (AV). In this paper we propose a classification approach to discriminate BCC from AV in dermoscopic images using an algorithm based on deep learning techniques.
A two-branch Convolutional Neural Network (CNN) is employed to construct the model. The first branch consists of CNN structures that process an RGB dermoscopic image while the second branch leverages the pre-trained ResNet18 network with an HSV dermoscopic image input. Both branches use Principal Component Analysis (PCA) normalization and image resizing. This concatenated architecture enables the system to exploit features extracted from both color spaces as well as CNN networks enhancing the overall performance of the model.
The proposed architecture is assessed using two public datasets. The first one is dedicated to the binary classification of Basal Cell Carcinoma (BCC) versus Acne Vulgaris (AV) achieving an accuracy of 99.06% a sensitivity of 98.73% and a specificity of 99.37%. The second dataset addresses multiclass classification along melanoma BCC and AV and achieves an accuracy of 97.17% a sensitivity of 97.13% and a specificity of 98.57%. The results highlight the effectiveness of the proposed model.
The dual-input hybrid algorithm based on convolutional neural networks and incorporating principal component analysis demonstrates promising results in distinguishing BCC from AV.
Preface
Patent Selections
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.
Navigating the Blockchain Revolution: Decentralization, Finance, and Beyond
A comprehensive and practice-oriented exploration of blockchain technology tracing its evolution from Bitcoin to Ethereum and beyond. This volume explains blockchain's foundational principles such as decentralization immutability and cryptographic security before moving into real-world applications across finance supply chain management healthcare governance and emerging tech ecosystems.
With contributions from leading researchers and practitioners the book examines both opportunities and challenges ranging from scalability and energy consumption to regulatory clarity and security. Readers also gain insights into cutting-edge developments like smart contracts decentralized finance (DeFi) identity systems Layer 2 solutions DAOs and blockchain's synergy with IoT and AI.
Key Features
Explains the core principles of blockchain from decentralization to cryptographic security.
Traces the evolution from Bitcoin and Ethereum to DeFi smart contracts and dApps.
Demonstrates applications across finance healthcare supply chain governance and identity management.
Addresses pressing challenges including scalability energy use regulatory frameworks and security threats.
Explores future trends like interoperability DAOs Layer 2 solutions and integration with IoT and AI.