Federated Learning (FL) enables collaborative training without centralizing sensitive data but faces challenges, including client authenticity, verifiable training participation, and secure aggregation. To overcome these challenges, we propose a novel framework, Zero-Knowledge Reputation-aware Blockchain Federated Learning (ZK-RBFL), which integrates blockchain, FL, Homomorphic Encryption (HE), and zero-knowledge proofs (ZKP). In the proposed ZK-RBFL framework, initially the clients undergo lightweight token-based authentication and then generate ZKP to provide cryptographic evidence of honest local training participation and reported inference accuracy before contributing their model updates. The model updates are encrypted using the CKKS HE mechanism to prevent any potential model inversion attacks. These encrypted model updates are stored on IPFS, with their corresponding CIDs recorded on the blockchain to ensure immutability. Further, ZK-RBFL enables mutual client verification of ZKPs to reduce server bottlenecks and enhance accountability. To ensure fairness and robustness in a distributed environment, we introduce a democratic blockchain consensus mechanism named Proof of Reputation-Weighted Voting (PoRWV) for block acceptance. Once consensus is reached, the encrypted model updates are aggregated using reputation-weighted averaging. We demonstrate the effectiveness of ZK-RBFL for brain tumor classification using a ZKP-compatible LeNet model for proof generation. Despite model simplicity, the global model achieves 94.22% accuracy. In addition, experiments with malicious clients and formal Scyther security analysis demonstrate that ZK-RBFL ensures both security and performance.

The integration of Artificial Intelligence (AI) into the medical field is revolutionizing healthcare by enabling efficient diagnostics and decision-making. A key challenge in medical AI is to ensure accurate predictions while maintaining data privacy and confidentiality. Several works have tried to address this challenge using Federated Learning (FL), but most of them are limited to simulated environments with no real-world implementation. We propose a real-time Websocket communication-based FL method for classifying medical images, which enables several hospitals to work together to train a deep learning (DL) model without disclosing private patient information. Each hospital, acting as a worker node, trains a local DL model on medical image datasets. Locally trained models generate weight parameters that are encrypted using homomorphic encryption (HE). These encrypted weights are then safely transmitted to a centralized master node, where they are aggregated to create an updated global DL model. The final model is then deployed back to the worker nodes, ensuring improved accuracy and generalization. Communications between the worker nodes and the master node are facilitated using WebSocket-based connections. Using FL and encryption techniques, this real-time implementation enhances model performance while preserving patient data confidentiality, demonstrating the practical feasibility of AI-driven secure healthcare solutions.

Effective diet management is crucial for preventing non-communicable diseases, including heart disease, diabetes, and obesity. This paper introduces NutriVision, an enhanced system featuring an interactive chatbot that delivers real-time dietary guidance and personalized recommendations. NutriVision enables users to inquire about nutritional content, receive customized recipe suggestions, and track nutritional goals through natural language interaction. By integrating user data such as health conditions, dietary preferences, and nutritional requirements, it generates tailored responses that enhance user engagement and decision-making. With the help of computer vision and machine learning, NutriVision accurately identifies food items and estimates quantities from smartphone-captured images, providing instant nutritional analysis that includes both macronutrient and micronutrient details with a 94% accuracy rate. It also offers step-by-step cooking instructions, suggests dietary alternatives, and answers queries related to nutrition and health. NutriVision refines its recommendations based on user feedback, creating a dynamic and responsive dietary management experience. Unlike conventional tools that rely on manual data entry or static food databases, NutriVision delivers a seamless, AI-driven interaction by combining food recognition with personalized dialogue. NutriVision exhibits high engagement, with a user engagement rate of 87% and response accuracy of 91%, demonstrating its effectiveness in fostering sustained interaction and delivering contextually relevant and accurate information. Designed with a user-friendly interface, NutriVision supports a variety of dietary frameworks, including vegetarian, vegan, and low-carb plans, ensuring its recommendations align with diverse user needs. By merging intelligent healthcare with interactive digital support, NutriVision empowers users to make informed dietary choices, promoting healthier eating habits. This paper also presents the system’s design, performance evaluation, and potential real-world applications, highlighting its contributions to personalized nutrition.

The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) is reshaping computational paradigms, creating the need for a new class of system software capable of managing hybrid workloads. This paper presents Q-OS, a Quantum-Aware Operating System designed to orchestrate hybrid quantum-classical AI pipelines across cloud and edge environments. Q-OS introduces three novel system components: a Quantum Scheduling Kernel that intelligently allocates portions of AI workloads (such as optimization or sampling) to quantum co-processors; a Quantum Resource Virtualization Layer that abstracts quantum devices as logical accelerators; and a Quantum-AI Compiler Interface that dynamically maps AI operations from frameworks such as TensorFlow and PyTorch into executable quantum kernels. Experimental evaluations using hybrid workloads-including large language model inference (The Pile dataset), quantum-classical generative adversarial networks (CelebA dataset), and reinforcement learning control (OpenAI Gym)-demonstrate that Q-OS achieves up to 32% reduction in end-to-end latency and 27% improvement in energy efficiency compared to classical distributed systems. The results indicate that Q-OS establishes a scalable foundation for the next generation of intelligent computing infrastructures, bridging quantum and AI ecosystems for real-world deployment.

Blockchain ecosystems, particularly decentralized finance (DeFi) platforms, have become high-value targets for adversaries exploiting vulnerabilities in smart contracts. Traditional honeypots designed to attract such attackers are often static and easily detectable once adversaries recognize repeating patterns. In this paper, we propose Adaptive AI-Powered Smart Contract Honeypots (AASCH), a novel deception-based security mechanism that dynamically evolves its behavior using reinforcement learning techniques. The honeypot continuously adapts to attacker strategies by modifying contract responses, transaction logic, and resource interactions in real time, thereby creating an unpredictable deception environment. We implement AASCH on the Ethereum test network and simulate various exploit scenarios, including reentrancy attacks, flash-loan exploits, and oracle manipulations. Experimental results demonstrate that AASCH significantly improves attacker capture rates (up to 37% higher than static honeypots) while reducing evasion attempts and false positives. Our findings indicate that adaptive deception is a promising defense strategy for strengthening blockchain ecosystems against evolving cyber threats.

Medical image classification using deep learning (DL) typically requires large and diverse datasets. However, data privacy regulations often limit data sharing across institutions. Federated Learning (FL) addresses this issue by enabling collaborative model training without transferring raw data. Despite its advantages, FL is challenged by limited data at each participating client, which can hinder model performance. To overcome this limitation, we employ Federated Transfer Learning (FTL), a hybrid approach that combines FL with Transfer Learning (TL) to improve model generalization under data scarcity. In this work, we apply FTL to chest X-ray (CXR) classification, leveraging MobileNet for one dataset and ResNet50 for another. We have evaluated our framework’s performance using various evaluation metrics. It achieved 98% accuracy and 99.97% AUC-ROC on Dataset1, and 93.46% accuracy with a 97.9% AUC-ROC on Dataset2, demonstrating its overall effectiveness. To enhance model interpretability, we use Explainable AI (XAI) techniques such as Grad-CAM and LIME to visualize decision-making. Furthermore, we employ two different GPT models-Gemini and ChatGPT-one for generating human-readable explanations based on the XAI visualizations and the other to quantitatively validate the reliability of the generated explanations on a five-point Likert scale. The proposed approach yielded reliability scores of 4.13 and 4.20 for GradCAM visualizations, and 4.43 and 4.87 for LIME visualizations, across the two datasets, indicating high reliability. Overall, the proposed FTL-XAI-GenAI framework ensures high classification performance and transparency, enabling medical professionals to understand AI-driven diagnoses while maintaining data privacy.

Double-circuit transmission line installations are rising due to their enhanced reliability, power transfer capability and operational flexibility, particularly in grids with significant share of power electronics-based sources. Reliable protection of these lines ensures the isolation of faulty sections, while precise fault location enables maintenance teams to quickly address the cause, thereby facilitating faster restoration and avoiding unnecessary curtailment of the clean power. Existing single-ended protection and fault location methods have limitations when applied to such double-circuit lines connected to power-electronics based renewable energy sources, mainly due to the source-dependent, variable, and controlled nature of their transient response. In this paper, limitations of existing methods when applied to such systems are demonstrated with several illustrative cases, and a reliable single-ended protection and an accurate fault location method are proposed. Protection requires the faulted line to be identified; this is achieved based on the polarity of the angle of an operating quantity evaluated at two extreme boundaries of the zone of protection. The operating quantity is defined as the apparent power flowing into the fault resistance path expressed as the function of fault location on the line of interest. Further, based on the same principle, accurate fault location is also identified without any additional measurement or complex calculations. Source-agnostic performance is accomplished through observability of the remote end using locally measured healthy line current. The proposed method is verified for multiple system configurations with power electronics-based resources including system with 100% such sources. The method is validated using experimental and field data and it is found to be reliable. Performance comparison with traditional distance, and existing single-ended protection and fault location methods for lines connected with power electronics-based resources are also conducted and improved performance is demonstrated.

The increasing use of electronic health records (EHRs) has transformed healthcare management, yet data sharing across institutions remains limited due to privacy concerns. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without centralized data sharing. However, non-independent and identically distributed (non-IID) data distributions, where the data across clients differ in class proportions and feature characteristics, pose a major challenge to achieving robust model performance. In this study, we propose a hybrid framework that combines the Federated Proximal (FedProx) algorithm with the ResNet50 architecture to address non-IID data issues. We artificially partitioned an IID brain tumor dataset into non-IID subsets to simulate real-world conditions and applied data augmentation techniques to balance class distributions. Global model performance is monitored across 100 training rounds with varying regularization parameters in FedProx. The proposed framework achieved an accuracy of 97.71% on IID data and 87.19% in extreme non-IID scenarios, with precision, recall, and F1-scores also demonstrating strong performance. These findings highlight the effectiveness of combining data augmentation with FedProx in mitigating data imbalance in FL, thereby supporting equitable and efficient training of privacy-preserving models for healthcare applications.

Purpose – The objective of this study is to analyse the integration of technology in Human Resources Management (HRM) with a special focus on Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Big Data. Design/methodology/approach – This study aims to contribute to the understanding of these trends by conducting a thorough bibliometric analysis using the Scopus database, encompassing research on HRM and Technology from 1991 to 2022. By employing citation analysis, co-citation analysis and co-word analysis, the study uncovers key patterns and trends in the field. Findings – The findings indicate that AI, Big Data and ML are the focal points of research when exploring the intersection of Technology and HRM. These technologies offer promising prospects for enhancing Human Resource processes, such as Talent Acquisition, Performance Management and Employee Engagement. Research limitations/implications – In our study, we showcase the practical implications that offer guidance for HR researchers and professionals, enabling them to make informed decisions regarding the adoption and implementation of Information Technology. Practical implications – This research can provide valuable insights to HR managers on the use of cutting-edge technology in HRM. It aims to enhance the manager’s awareness of how technology-enabled HRM can improve HR performance. Originality/value – This study adds to the existing body of knowledge on how Modern Technology empowers HRM. It also proposes a conceptual framework for the use of Modern Technology along with Strategic Management and Knowledge Management to improve Human Resource Performance.

Brain tumor diagnosis using magnetic resonance imaging (MRI) scans is critical for improving patient survival rates. However, automating the analysis of these scans faces significant challenges, including data privacy concerns and the scarcity of large, diverse datasets. A potential solution is federated learning (FL), which enables cooperative model training among multiple organizations without requiring the sharing of raw data; however, it faces various challenges. To address these, we propose Federated Adaptive Reputation-aware aggregation with CKKS (Cheon-Kim-Kim-Song) Homomorphic encryption (FedARCH), a novel FL framework designed for a cross-silo scenario, where client weights are aggregated based on reputation scores derived from performance evaluations. Our framework incorporates a weighted aggregation method using these reputation scores to enhance the robustness of the global model. To address sudden changes in client performance, a smoothing factor is introduced, while a decay factor ensures that recent updates have a greater influence on the global model. These factors work together for dynamic performance management. Additionally, we address potential privacy risks from model inversion attacks by implementing a simplified and computationally efficient CKKS homomorphic encryption, which allows secure operations on encrypted data. With FedARCH, encrypted model weights of each client are multiplied by a plaintext reputation score for weighted aggregation. Since we are multiplying ciphertexts by plaintexts, instead of ciphertexts, the need for relinearization is eliminated, efficiently reducing the computational overhead. FedARCH achieved an accuracy of 99.39%, highlighting its potential in distinguishing between brain tumor classes.Several experiments were conducted by adding noise to the clients’ data and varying the number of noisyclients.Anaccuracy of 94% was maintained even with 50% of noisy client satahigh noise level,while the standard FL approach accuracy dropped to 33%.Our results and the security analysis demonstrate the effectiveness of FedARCH in improving model accuracy, its robustness to noisy data,and its ability to ensure data privacy,makingitaviableapproach for medical image analysis in federated settings.

Artificial intelligence (AI) has successfully transformed various fields through automation, yet it continues to struggle in some fields, particularly in the medical field. It is due to the lack of large, accessible datasets, because medical institutions often limit data sharing to ensure the confidentiality and safety of critical patient data. An application for multi-class brain tumor classification that uses a privacy-preserving Federated Transfer Learning (FTL) platform is proposed to get over these ethical and privacy concerns. It integrates the TL with the privacy-preserving FL. Within our system, the exchange of information among various healthcare institutions does not need the direct sharing of raw patient data, hence ensuring the protection of patient privacy. In the federated learning context with two transfer learning models, VGG16 and VGG19, the accuracies are 93.11% and 91.8%, respectively. The results of our study indicate that privacy can be maintained without compromising performance and emphasize the potential of our proposed FTL framework to safeguard privacy in healthcare systems that are propelled by AI.

Stroke is one of the most significant health problems that aid disability and mortality in functionality of our system. Although there are already varies approaches, traditional and technical methods developed to help patients overcome these challenges, common systems to treat and help patients recover from this are, using local therapy approach, using EEG (electroencephalogram), and or using the brain computer interface. However, not all patients find this approach applicable, suitable and affordable. The aim of this research is to perform PostStroke Rehabilitation recovery training and testing on the patient without the need for brain computer interface (BCI) and electroencephalogram (EEG); our proposed method provides similar embodiment and rehabilitation capabilities to those perceived from existing therapeutic techniques such as Constraint-induced movement therapy (CIMT), occupational therapy, mirror therapy and authenticating fear. Our Research approach resolution is to develop a virtual reality (VR) based system for poststroke rehabilitation using Meta Quest (Headset) and Unity game engine for simulation of the virtual environment. Our emerging methods in Integrating of Generative-AI to provide adaptive therapy recommendations based on patient movements and engagement levels. We implement the use of a real-time compensatory movement detection system without EEG, relying on AI-driven motion analysis.

The integration of artificial intelligence (AI) in healthcare has opened transformative avenues for enhancing patient care, decision-making, and accessibility to medical knowledge. This work presents a dual-function generative AI-based system that bridges the gap between complex clinical documentation and patient understanding. The system comprises two core modules: a Medical Report Analyzer that interprets unstructured and structured clinical data into patient-friendly insights, and a domain-specific Healthcare Chatbot trained on authoritative medical literature. By leveraging OpenAI's large language models (LLMs) and semantic search with Pinecone vector embeddings, the system delivers accurate, context-aware responses and explanations. It simplifies clinical jargon, detects anomalies, and enables interactive health education. Built on a Flask-based architecture, this unified platform supports both real-time document analysis and conversational interactions. Evaluation results demonstrate strong performance in interpretability, relevance, and user engagement. This work lays a foundation for scalable, patient-centric AI solutions that enhance health literacy and empower informed decision-making.

Nowadays, Federated learning (FL) has surfaced as an auspicious approach that allows collaborative machine learning in smart healthcare systems without compromising data privacy. FL has incredible advantages, but still there are a number of security and privacy issues that need to be resolved before it can be used in smart healthcare settings in a safe and competent manner. The security and privacy concerns of FL technology in smart healthcare systems are thoroughly reviewed in this chapter. This chapter will present a careful explanation of some remarkable problems that FL presents, such as data privacy, communication security, model poisoning, and the reliability of involved parties. We also examine current ideas and plans put forth to lessen these difficulties, including blockchain-based methods, safe aggregation, and differential privacy. To further strengthen the security and privacy of FL in smart healthcare systems, we also outline future research topics and possible areas for improvement. Additionally, a comparative analysis is mentioned in this chapter, which clearly differentiates between this chapter's contribution and existing research work on this area. Researchers, medical professionals, and legislators may all benefit from this work's insightful analysis of the security and privacy concerns associated with FL and its potential solutions for smart healthcare settings.

Non-Fungible Tokens (NFTs) represent a revolutionary class of digital assets, characterized by their uniqueness and value derived from metadata and visual traits. However, NFT markets suffer from volatility and a lack of transparent valuation systems, making it difficult for collectors and investors to estimate asset worth. This paper presents a comprehensive machine learning pipeline for predicting the market value of NFTs based on trait rarity and sale metadata. We apply rigorous preprocessing, compute rarity scores from trait distributions, and compare multiple regression models, including Random Forest, LightGBM, CatBoost, and Extra Trees. Our analysis demonstrates that tree-based models significantly outperform simpler regressors, with Extra Trees achieving the lowest RMSE of 136.91 and the highest $R^2$ score of $1. 0$. Visual and statistical analyses further validate the effectiveness of our methodology in predicting NFT prices with high precision.

Urban traffic congestion at signalized intersections is a significant challenge due to the inflexibility of fixed-time control systems. While Reinforcement Learning (RL) offers optimization, it comes with complexities in data, interpretability, and deployment. This report presents a heuristic-based adaptive traffic signal control (ATSC) system within a microscopic Pygame simulation. The system uses dynamic green time calculation and phase prioritization based on a weighted score of maximum waiting time, queue length, and an aging mechanism. Comparative analysis against a fixed-time baseline showed improvements, including a 36.9% reduction in average vehicle waiting time and a 17.6% increase in intersection throughput. The architecture supports integration with computer vision (YOLO/CNN) for realworld input.

Accurate classification of single nucleotide poly-morphisms (SNPs) is essential for genomic medicine. In this study, we fine-tune DNABERT, a transformer pre-trained on the human genome using k-mer embeddings for SNP pathogenicity prediction. Using a curated dataset of 50,000 Clin Var variants, our model achieves 94 % training accuracy, 93.4 % validation accuracy, an Fl-score of 0.938, and an AUC of 0.96. To improve interpretability, we integrate explainable AI (XAI) methods including Layer-Wise Relevance Propagation (LRP), Integrated Gradients, and DeepLIFT. These are evaluated using fidelity, completeness, robustness, and biological relevance metrics. LRP consistently delivers the most interpretable and biologically aligned attributions. Our findings show that combining fine-tuned DNABERT with effective XAI enables a transparent and scalable framework for SNP classification in clinical genomics.

Brain tumor classification via MRI scans poses challenges related to privacy, data decentralization, and model transparency. This work proposes a secure, decentralized pipeline combining Federated Learning (FL) with blockchain-based ver-ification for multi-class tumor detection. Using ResNet-SO with transfer learning, the model distinguishes glioma, meningioma, and pituitary tumors from colorized MRI images. Grayscale scans are enhanced using histogram equalization and edge sharpening to align with RGB-pretrained networks. Training is distributed across simulated hospital nodes via the Flower FL framework, leveraging Federated Batch Normalization (FedBN) to address non-IID data. A smart contract on Ethereum's Sepolia testnet records model hashes, accuracy, and timestamps for auditability. The approach achieves 97.98 % global accuracy, outperforming CNN baselines. This system offers a scalable, transparent, and privacy-preserving solution for AI-driven med-ical imaging.

Low-frequency oscillations (LFOs) are a growing concern for power system stability, as they can degrade power quality, damage equipment, and even trigger blackouts. These oscillations, typically in the 0.01-4 Hz range, are becoming more unstable with increasing integration of inverter-based resources (IBRs) and the prevalence of weak grid conditions. This paper analyzes three recent LFO events observed in the North-Eastern Region (NER) grid of India. Using the matrix pencil method for mode identification and the dissipating energy flow method for source localization, the study examines oscillation characteristics, damping behavior, and origin. The results show that IBR control actions, STATCOM reactive-power regulation, generator control responses, and inter-regional tie-line interactions all influence the observed oscillations. A key factor worsening stability is the high impedance of the NER grid. Grid strength assessment, based on short-circuit capacity relative to the ±800 kV HVDC system rating at Biswanath Chariali, gives a ratio of about 2.34 in the Assam system, indicating weak conditions. With around 4.6% solar penetration by installed capacity, the study highlights the need for enhanced monitoring, advanced analytical techniques, and coordinated mitigation strategies to ensure secure operation of future renewable-rich grids.

India is witnessing rapid growth in solar photovoltaic (PV) deployment as part of its transition toward a decarbonized energy system. However, most PV capacity is currently concentrated in a few western and southern states, while the North-Eastern Region (NER) remains largely untapped despite its significant solar and hydroelectric potential. This paper investigates the technical and operational implications of integrating solar PV in the NER, focusing on actual fault events that highlight grid protection challenges under high inverter-based resource (IBR) penetration. By analyzing disturbance data, the study examines issues such as non-homogeneity in fault characteristics, weak infeed scenarios, and the limitations of conventional distance protection. Furthermore, it demonstrates the advantages of geographically dispersing PV generation across India to reduce variability, extend solar generation hours, and improve the diversity factor. Results show that NER solar output aligns closely with national morning peak demand, thereby reducing the need for costly storage or gas-fired generation. The combined utilization of PV and hydro resources in NER can further optimize existing transmission infrastructure. The insights presented in this paper contribute to ongoing discussions on adaptive protection schemes, grid code compliance, and strategic PV deployment to enhance resilience, stability, and sustainability of the Indian power system.

Transfer learning is a set of techniques to apply skills or knowledge from a source task to a target task that is different but related, while Hybrid Quantum-Classical Transfer Learning (HQCTL) model extends the skills learned with quantum feature extraction specifically for edge computing which lacks resources. HQCTL combined with quantum-derived characteristics enhances accuracy, time, and real-time computation when it comes to classical aspects such as object detection or image analysis. In experimenting with images datasets such as COCO and PASCAL VOC the distribution of the framework generally presented higher accuracy and lower costs in terms of computation compared to either a purely classical or quantum approach. Of course, quantum enhanced feature extraction is still far from known and has greater potential for HQCTL as it helps to advance data representation which is optimal for the strict real-time processing in the IoT periphery. Possible research avenues include the development of different quantum representations of the problem, enhancements of the approach interconnectivity with various edge substrates, and the application of the framework to new machine learning tasks such as video analysis and time series prediction. Through presenting the HQCTL framework the potential of hybrid quantum-classical models to enhance edge AI applications while offering reliability, scalabilty and efficiency is demonstrated.

This article presents a new model, ALL-Net, for the detection of acute lymphoblastic leukemia (ALL) using a custom convolutional neural network (CNN) architecture and explainable Artificial Intelligence (XAI). A dataset consisting of 3,256 peripheral blood smear (PBS) images belonging to four classes—benign (hematogones), and the other three Early B, Pre-B, and Pro-B, which are subtypes of ALL, are utilized for training and evaluation. The ALL-Net CNN is initially designed and trained on the PBS image dataset, achieving an impressive test accuracy of 97.85%. However, data augmentation techniques are applied to augment the benign class and address the class imbalance challenge. The augmented dataset is then used to retrain the ALLNet, resulting in a notable improvement in test accuracy, reaching 99.32%. Along with accuracy, we have considered other evaluation metrics and the results illustrate the potential of ALLNet with an average precision of 99.35%, recall of 99.33%, and F1 score of 99.58%. Additionally, XAI techniques, specifically the Local Interpretable Model-Agnostic Explanations (LIME) algorithm is employed to interpret the model’s predictions, providing insights into the decision-making process of our ALL-Net CNN. These findings highlight the effectiveness of CNNs in accurately detecting ALL from PBS images and emphasize the importance of addressing data imbalance issues through appropriate preprocessing techniques at the same time demonstrating the usage of XAI in solving the black box approach of the deep learning models. The proposed ALL-Net outperformed EfficientNet, MobileNetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, and NASNetLarge except for DenseNet201 with a slight variation of 0.5%. Nevertheless, our ALL-Net model is much less complex than DenseNet201, allowing it to provide faster results. This highlights the need for a more customized and streamlined model, such as ALL-Net, specifically designed for ALL classification. The entire source code of our proposed CNN is publicly available at https://github.com/Abhiram014/ALL-Net-Detection-of-ALLusing-CNN-and-XAI.

Images transmitted through internet can be easily tampered by the available image editing tools. This article proposes a Hamming code based watermarking approach for tamper localization and correction of images. The original image is divided into various blocks with 8 consecutive pixels. The 64 bits of the 8 pixels are arranged into an 8 × 8 matrix of bits. A modified (7,4) Hamming code (MHC) is applied on first 7 most significant bits (MSBs) of each row of the matrix. The first 4 MSBs are data bits. The next 3 bits are redundant bits. The watermark bits are calculated from the 4 MSBs and stored in 3 redundant bits. Furthermore, the column parity for the first 7 columns of the 8 × 8 matrix is computed and embedded in the least significant bits (LSBs) of the 7 rows. Thereafter the column parity of the first 7 bits of 8th column is stored in 8th bit location of 8th column. This technique can detect 1-bit error or 2-bit error if it occurs in one of the 8 pixels of the block. Experimental outcomes prove that this proposed scheme maintains 4.0 bits per pixel with 36.94 dB peak signal-to-noise ratio (PSNR) and 0.9781 structural similarity (SSIM).

Early adoption of Internet of Medical Things (IoMT) are enhancing the healthcare sector in all directions. Though the advances are adding advantages to the existing systems, the security and privacy of medical data remain a challenge. The increase in IoMT and mobile healthcare devices presence on untrusted networks can make the situation more complicated for healthcare system users. Moreover, they are pushing critical data to centralized locations like cloud, where the patient lacking control on his data. In this regard, a secure IoT framework is desirable which is capable of preserving the integrity and confidentiality of the medical data. Due to this, we proposed a novel architecture which leverages blockchain, IPFS, zero-knowledge protocols, and functional encryption technologies to provide decentralised healthcare system privacy and security. The proposed system helps the healthcare system administrators maintain data confidentiality, availability, integrity, and transparency over an untrusted peer-to-peer network without any human interference. Moreover, the system eliminates the requirement for a centralised server for functional encryption operations using hybrid computing paradigms. Finally, the proposed system suggests a novel mechanism to minimise the latency in data sharing over the network without compromising data security and privacy. To describe the working principle of this architecture a logical analysis is carried out which shows that the system is capable of providing the desired security and privacy.

This research presents CommuniWeave, a social media platform designed to foster meaningful, conversation-driven interactions through threaded post discussions. Unlike conventional social media platforms that emphasize fast-changing content, CommuniWeave addresses the need for substantial connections by allowing users to engage in focused, post-based dialogues within chosen social circles. The platform empowers users to select the people most important to them for inclusion in personalized "Threads," creating a dedicated space for genuine, lasting interactions. Core functionalities include the creation and management of posts, threaded comment support, and profile customization to enhance user engagement and content quality. Through a user-centric design, CommuniWeave contributes a distinctive approach to social networking that emphasizes thoughtful communication and connection, supporting a shift toward more valuable online interactions.

In this research paper, we tackle the challenge of accurately diagnosing Parkinson’s disease (PD) using machine learning (ML) techniques, with a specific focus on addressing imbalanced datasets. We employ Adaptive Synthetic Sampling (ADASYN) to intelligently balance class representation, ensuring that minority groups, which are crucial for precise PD detection, are included. Additionally, we utilize min-max scaling to rescale features and incorporate various ML models, such as XGBoost, to leverage their unique strengths. Our findings underscore the effectiveness of this integrated approach in accurately identifying Parkinson’s disease. Evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate the robust performance of our model. Visualization tools like the Confusion Matrix and Receiver Operating Characteristic (ROC) curve provide detailed insights into the capabilities of our model and areas for improvement. Significantly, our model achieves exceptional accuracy (97.44%) and precision (100%) in detecting Parkinson’s disease, surpassing alternative algorithms.

Chronic renal disease (CRD) is a significant concern in the field of healthcare, highlighting the crucial need of early and accurate prediction in order to provide prompt treatments and enhance patient outcomes. This article presents an end-to- end predictive model for the binary classification of CRD in healthcare, addressing the crucial need for early and accurate predictions to enhance patient outcomes. Through hyperparameter optimization using GridSearchCV, we significantly improve model performance. Leveraging a range of machine learning (ML) techniques, our approach achieves a high predictive accuracy of 99.07% for random forest, extra trees classifier, logistic regression with L2 penalty, and artificial neural networks (ANN). Through rigorous evaluation, the logistic regression with L2 penalty emerges as the top performer, demonstrating consistent performance. Moreover, integration of Explainable Artificial Intelligence (XAI) techniques, such as Local Interpretable Model- agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), enhances interpretability and reveals insights into model decision-making. By emphasizing an end-to-end model development process, from data collection to deployment, our system enables real-time predictions and informed healthcare decisions. This comprehensive approach underscores the potential of predictive modeling in healthcare to optimize clinical decision-making and improve patient care outcomes.

In the face of global crises, humanitarian aid organizations are crucial for delivering assistance to vulnerable populations. However, the effectiveness of aid operations is hindered by challenges in transparency, accountability, and operational efficiency. This paper explores the transformative potential of blockchain technology in revolutionizing humanitarian aid delivery. Blockchain, with its decentralized and immutable ledger system, promises to address issues of trust and transparency. The research outlines a strategic vision to enhance supply chain transparency, improve operational efficiency and foster accountability within the humanitarian aid sector. A key focus is the development and implementation of the”HumanitarianAid” smart contract, leveraging blockchain to automate and ensure equitable resource distribution. The paper details the system design, transaction flow, and deployment using Remix IDE, presenting successful testing scenarios. The visual representation of executed transactions in the blockchain model illustrates dynamic interactions between donors, NGOs, and beneficiaries. The findings underscore the potential of blockchain to in shaping the future of humanitarian aid operations, streamline processes, enhance transparency, and facilitate efficient and fair humanitarian aid delivery.

COVID-19 has severely impacted healthcare systems and economies worldwide since its onset in late 2019. Rapid and accurate diagnosis is vital to control the spread. The golden standard for testing is reverse transcription polymerase chain reaction (RT-PCR), yet it has drawbacks. As an alternative, chest radiography-based diagnosis presented results near to the RT-PCR. The study proposes a Transfer Learning(TL)-based approach for classifying images of chest X-ray into normal, COVID-19, and pneumonia categories, using data from two publicly available Kaggle datasets. After the preprocessing, seven pretrained Convolutional Neural Networks (CNNs) including ResNet50, ResNet101, VGG16, VGG19, InceptionV3, MobileNet and Xception are fine-tuned by adding new fully connected layers. MobileNet achieved best accuracy of 96.21% on one dataset while ResNet50 attained 94.86% on the second dataset. High precision, recall and F1-scores are also obtained. The consistent performance across CNN architectures demonstrates the effectiveness of TL in COVID-19 detection from chest radiographs, presenting a rapid and reliable solution for diagnosis.

Globalization has led to complex, cloud-centric supply chains that require transparency and traceability in the manufacturing process. However, traditional supply chain models are vulnerable to single points of failure and lack a people-centric approach. To address these challenges, our proposed work presents an innovative healthcare supply chain model that utilizes blockchain technology combined with Zero Knowledge Proofs (zk-SNARKs) and role-based access control (RBAC) mechanisms. The addition of RBAC to the proposed model ensures that only authorized users can access certain data and functionalities within the system, while improving the security and access control. This approach guarantees secure storage of business-sensitive data while enabling real-time product tracking and traceability. The proposed model was tested using an Ethereum-based decentralized application (DApp), demonstrating the preservation of digital record integrity, availability, and scalability by eliminating single points of failure. The system also offers privacy and security for sensitive data through the use of zk-SNARKs. In case of product faults, the model enables error tracing without disclosing the entire data set through the use of document hashes. By incorporating RBAC access control mechanisms, our proposed solution offers an effective, secure, and privacy-preserving mechanism for handling sensitive data, also benefiting stakeholders in the supply chain ecosystem.

While currently in practice, classical encryption methods such as RSA and AES-256 are opportunities in the age of quantum computing, algorithms such as Shor’s are capable of bringing down their fundamental security. Unfortunately, these threats have not been well addressed by current technologies and hence Post-Quantum Cryptography (PQC) seeks to offer solutions that provide protections from quantum attacks. HQCE, the Hybrid Quantum-Classical Encryption algorithm combines AES-256 for encryption of data with LWE problem for quantum secure keying. HQCE derives a 256-bit AES key for data encryption and for protecting this generated AES key, it employs LWE making sure that the data is safe from any miscreants. In the decryption process the LWE layer decode the AES key so that only authorized personnel can decrypt the data. In this paper, I present the encryption and decryption mechanisms of HQCE with a security assessment and demonstrate that HQCE is a post-quantum security solution that is resistant to both classical and quantum adversaries.

Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchain's scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain-based Privacy-preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs' services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.

Light trail is an unidirectional optical bus within WDM mesh networks that can transmit data between the convener (start) and the end node, and allows the intermediate nodes to participate in data communication. Recently, it is seen that splitting the light trail can improve the networks' performance. However, the entire communication can come to an end, if a link failure occurs. In this paper we have proposed an algorithm namely, Split Light Trail Protection (SLTP) for protection of split light trail(s) against adjacent multiple link failures as well as non-adjacent multiple link failures, within the WDM mesh networks. Our proposed algorithm works for both static and dynamic split light trail(s). Recent studies shows that no previous works has been carried out for the protection of split light trail(s). Simulations in Matlab has been performed to calculate the blocking probability and throughput generated before and after the protection of split light trail(s). The time complexity calculated for the proposed algorithm SLTP is O(n3).

Multicasting is inevitable in the advent of the age of online video streaming. Light trail, the unidirectional optical bus, enhances multicasting by facilitating sub wavelength granularity. Splitting a light trail into segments increases the bandwidth utilization. To remove the overburden of the auxiliary graph of the existing works lead the motivation of the proposed heuristic algorithm known as Dynamic Multicast Traffic Grooming and Split Light Trail Assignment (DMTG-SLTA), which further aims to reduce the blocking probability and other network resources while satisfying the dynamic connection requests. Simulation results are verified though numerical analysis and compared to the existing well known algorithms, thereby concluding the proposed work to be successful only after minimizing its computational complexity.

The 3rd Generation Partnership Project (3GPP) has implemented the Authentication and Key Agreement (AKA) protocol in 5G communication networks to ensure user equipment privacy. Due to the high density and concurrent communication, the primary goal is to provide an efficient authentication for massive enhanced Machine Type Communication (eMTC) devices. However, certain security flaws have been discovered in the 5G-AKA protocol, and no scheme has yet been developed that meets the needs of a group of eMTC devices, such as signaling congestion avoidance, key forward/backward secrecy (KFS/KBS) establishment, resistance against malicious attacks, and session key secrecy. Furthermore, the current group-based 5G communication network techniques do not require the group membership update mechanism at each device joining or leaving the group. To address these issues, we present a lightweight and secure technique for assembling a secure ecosystem of eMTC devices in a 5G network. The protocol employs the incremental hash mechanism to complete group member joining/leaving activities. For a thorough assessment of LS-AKA, the Random Oracle Model (ROM) is used to do formal security proofs, and informal security analysis shows that it is resistant to malicious attacks. In addition, the performance and simulation results of the existing and suggested protocols in terms of signaling, communication, and computing overhead are analyzed. According to the assessment results, the LS-AKA protocol improves the privacy and confidentiality of the 5G-enabled smart environment.

The Internet of Medical Things (IoMT) technology's fast advancements aided smart healthcare systems to a larger extent. IoMT devices, on the other hand, rely on centralized processing and storage systems because of their limited computational and storage capacity. The reliance is susceptible to a single point of failure (SPoF) and erodes the user control over their medical data. In addition, Cloud models result in communication delays, which slow down the system's overall reaction time. To overcome these issues a decentralized distributed smart healthcare system is proposed that eliminates the SPoF and third-party control over healthcare data. Additionally, the proposed Fortified-Chain 2.0 uses a blockchain-based selective sharing mechanism with a mutual authentication technique to solve the issues, such as data privacy, security, and trust management in decentralized peer-to-peer healthcare systems. Also, we suggested a hybrid computing paradigm to deal with latency, computational, and storage constraints. A novel distributed machine learning (ML) module named random forest support vector machine (RFSVM) also embedded into the Fortified-Chain 2.0 system to automate patient health monitoring. In the RFSVM module, a random forest (RF) is used to select an optimal set of features from patients data in real-time environment and also support vector machine (SVM) is used to perform the decision making tasks. The proposed Fortified-Chain 2.0 works on a private blockchain-based distributed decentralised storage system (DDSS) that improves the system-level transparency, integrity, and traceability. Fortified-Chain 2.0 outperformed the existing Fortified-Chain in terms of low latency, high throughput, and availability with the help of a mutual authentication method.

The recent COVID-19 pandemic has underlined the significance of digital health record management systems for pandemic mitigation. Existing smart healthcare systems (SHSs) fail to preserve system-level medical record openness and privacy while including mitigating measures such as testing, tracking, and treating (3T). In addition, current centralised compute architectures are susceptible to denial of service assaults because of DDoS or bottleneck difficulties. In addition, these current SHSs are susceptible to leakage of sensitive data, unauthorised data modification, and non-repudiation. In centralised models of the current system, a third party controls the data, and data owners may not have total control over their data. The Coviblock, a novel, decentralised, blockchain-based smart healthcare assistance system, is proposed in this study to support medical record privacy and security in the pandemic mitigation process without sacrificing system usability. The Coviblock ensures system-level openness and trustworthiness in the administration and use of medical records. Edge computing and the InterPlanetary File System (IPFS) are recommended as part of a decentralised distributed storage system (DDSS) to reduce the latency and the cost of data operations on the blockchain (IPFS). Using blockchain ledgers, the DDSS ensures system-level transparency and event traceability in the administration of medical records. A distributed, decentralised resource access control mechanism (DDRAC) is also proposed to guarantee the secrecy and privacy of DDSS data. To confirm the Coviblock’s real-time behaviour on an Ethereum test network, a prototype of the technology is constructed and examined. To demonstrate the benefits of the proposed system, we compare it to current cloud-based health cyber–physical systems (H-CPSs) with blockchain. According to the experimental research, the Coviblock maintains the same level of security and privacy as existing H-CPSs while performing considerably better. Lastly, the suggested system greatly reduces latency in operations, such as 32 milliseconds (ms) to produce a new record, 29 ms to update vaccination data, and 27 ms to validate a given certificate through the DDSS.

The recent COVID-19 outbreak highlighted the requirement for a more sophisticated healthcare system and real-time data analytics in the pandemic mitigation process. Moreover, real-time data plays a crucial role in the detection and alerting process. Combining smart healthcare systems with accurate real-time information about medical service availability, vaccination, and how the pandemic is spreading can directly affect the quality of life and economy. The existing architecture models are become inadequate in handling the pandemic mitigation process using real-time data. The present models are server-centric and controlled by a single party, where the management of confidentiality, integrity, and availability (CIA) of data is doubtful. Therefore, a decentralised user-centric model is necessary, where the CIA of user data is assured. In this paper, we have suggested a decentralized blockchain-based pandemic detection and assistance system (iBlock). The iBlock uses robust technologies like hybrid computing and IPFS to support system functionality. A pseudo-anonymous personal identity is introduced using H-PCS and cryptography for anonymous data sharing. The distributed data management module guarantees data CIA, security, and privacy using cryptography mechanisms. Furthermore, it delivers useful intelligent information in the form of suggestions and alerts to assist the users. Finally, the iBlock reduces stress on healthcare infrastructure and workers by providing accurate predictions and early warnings using AI/ML.

Distributed denial of service (DDoS) attacks have always been a nightmare for network infrastructure for the last two decades. Existing network infrastructure is lacking in identifying and mitigating the attack due to its inflexible nature. Currently, software-defined networking (SDN) is more popular due to its ability to monitor and dynamically configure network devices based on the global view of the network. In SDN, the control layer is accountable for forming all decisions in the network and data plane for just forwarding the message packets. The unique property of SDN has brought a lot of excitement to network security researchers for preventing DDoS attacks. In this article, for the identification of DDoS attacks in the OpenFlow controller, a probabilistic technique with a central limit theorem has been utilized. This method primarily detects resource depletion attacks, for which the DARPA dataset is used to train the probabilistic model. In different attack scenarios, the probabilistic approach outperforms the entropy-based method in terms of false negative rate (FNR). The emulation results demonstrate the efficacy of the approach, by reducing the FNR by 98% compared to 78% in the existing entropy mechanism, at 50% attack rate.

The rapid developments in the Internet of Medical Things (IoMT) help the smart healthcare systems to deliver more sophisticated real-time services. At the same time, IoMT also raises many privacy and security issues. Also, the heterogeneous nature of these devices makes it challenging to develop a common security standard solution. Furthermore, the existing cloud-centric IoMT healthcare systems depend on cloud computing for electrical health records (EHR) and medical services, which is not suggestible for a decentralized IoMT healthcare systems. In this article, we have proposed a blockchain-based novel architecture that provides a decentralized EHR and smart-contract-based service automation without compromising with the system security and privacy. In this architecture, we have introduced the hybrid computing paradigm with the blockchain-based distributed data storage system to overcome blockchain-based cloud-centric IoMT healthcare system drawbacks, such as high latency, high storage cost, and single point of failure. A decentralized selective ring-based access control mechanism is introduced along with device authentication and patient records anonymity algorithms to improve the proposed system's security capabilities. We have evaluated the latency and cost effectiveness of data sharing on the proposed system using Blockchain. Also, we conducted a logical system analysis, which reveals that our architecture-based security and privacy mechanisms are capable of fulfilling the requirements of decentralized IoMT smart healthcare systems. Experimental analysis proves that our fortified-chain-based H-CPS needs insignificant storage and has a response time in the order of milliseconds as compared to traditional centralized H-CPS while providing decentralized automated access control, security, and privacy.

The COVID-19 outbreak highlighted the smart healthcare infrastructure requirement to speed up vaccination and treatment. Present vaccination supply chain models are fragmented in nature, and they are suitable for a pandemic like COVID-19. Most of these vaccination supply chain models are cloud-centric and depend on humans. Due to this, the transparency in the supply chain and vaccination process is questionable. Moreover, we con't trace where the vaccination programs are facing issues in real-time. Furthermore, traditional supply chain models are vulnerable to a single point of failure and lack people-centric service capabilities. This paper has proposed a novel supply chain model for COVID-19 using robust technologies such as Blockchain and the Internet of Things. Besides, it automates the entire vaccination supplication chain, and it records management without compromising data integrity. We have evaluated our proposed model using Ethereum based decentralized application (DApp) to showcase its real-time capabilities. The DApp contains two divisions to deal with internal (intra) and worldwide (inter) use cases. From the system analysis, it is clear that it provides digital records integrity, availability, and system scalability by eliminating a single point of failure. Finally, the proposed system eliminates human interference in digital record management, which is prone to errors and alternation.

Globalization has made supply chain business management more complicated over time. The existence of intermediary parties in the supply chain causes major issues like product genuineness, as well as transparency in product quality and quantity information management, etc. Traditional supply chain models depend on intermediaries and also are cloud-based systems. It is very much difficult to track the data state changes across the supply chain's larger network. Latest technologies such as blockchain and the Internet of Things (IoT) play a critical role in bringing transparency to supply chain management. In this paper, we have proposed FarmersChain, a novel decentralized data-centric smart supply chain management system based on blockchain and IoT technologies. In our proposed system FarmerChain, smart contracts are used to automate digital agreements. It was examined and analyzed on a local testbed to demonstrate its potential. Based on the system analysis and testing, we discovered that the proposed supply chain management is feasible in a real-time environment without the interference of a third party and middleman. It also ensures the product's quality and quantity information status is accurate, accessible, and transparent.

Because of urbanization and industrialization, non-biodegradable garbage is growing at an exponential rate. Industries have their own waste management and treatment divisions to take care of their waste products. However, civilian entities are facing many issues in waste management due to the lack of proper systems for segregating waste materials. This article proposed a unique smart waste management system using Blockchain and Internet of Things (IoT) to simplify the waste segregation with the help of smart bins. The proposed system distributes rewards to users for proper disposal of waste into smart bins using smart contracts. We deployed a prototype model on different test networks to compare its real-time performance. From the experimental analysis, we can conclude that the proposed model performs better on the Matic test network than the Binance Smart Chain (BSC) and Ropsten test networks. Finally, the proposed solution ensures system transparency, traceability, and scalability, as well as eliminating single points of failure (SPoF).

For the last few decades, fiber optic cables not only replaced copper cables but also made drastic evolution in the technology to overcome the optoelectronic bandwidth mismatch. Light trail concept is such an attempt to minimize the optoelectronic bandwidth gap between actual WDM bandwidth and end user access bandwidth. A light trail is an optical bus that connects two nodes of an all optical WDM network. In this paper, we studied the concept of split light trail and proposed an algorithm namely Static Multi-Hop Split Light Trail Assignment (SMSLTA), which aims to minimize blocking probability, the number of static split light trails assigned and also the number of network resources used, at the same time maximizing the network throughput. Our proposed algorithm works competently with the existing algorithms and generates better performance in polynomial time complexity.

Light trail is a unidirectional optical bus between the source and the destination node of a WDM network. In this paper, a novel algorithm is proposed for dynamic light trail assignment, which competently works for unicast dynamic connection requests. The routing is based on Hoffman k-shortest path algorithm. The proposed algorithm is solvable in polynomial time complexity and generates better results when compared with other existing algorithms. The existing algorithms are either dependent on the complex auxiliary graph, or they have a huge run time complexity. This motivated us to lay down our research work, which is free from the complex auxiliary graph and works in lesser time complexity. The aim of the paper is to satisfy the dynamic connection requests by assigning minimum number of dynamic light trails with the objective of minimizing the blocking probability, while maximizing the capacity utilization of each dynamic light trail assigned.

This paper presents a new hybrid image watermarking method based on IWT, DCT, and SVD domains, to solve the problem of false-positive detection and scale down the impact of geometric attacks. Properties of IWT, DCT, and SVD enable in achieving higher imperceptibility and robustness. However, SVD-based watermarking method suffers from a major flaw of false-positive detection. Principal component of watermark is embedding in the cover image to overcome this problem. Attacker cannot extract watermark without the key (eigenvector) of the embedded watermark. To recover geometrical attacks, we use a synchronization technique based on corner detection of the image. Computer simulations show that the novel method has improved performance. A comparison with well-known schemes has been performed to show the leverage of the proposed method.

Smart healthcare monitoring systems provide better healthcare service by improving the availability and transparency of health data. However, it also posses serious threats to data security and privacy. As medical internet of things (IoT) are connected to other devices through various networks that provide a suitable attack surface for the intruders. Further, the health data are sensitive, and any breach in security may lead to wrong treatment or compromising the privacy of the patients. In this regard, a secure IoT frame is desirable, which is capable of preserving the integrity and confidentiality of the medical data. In this paper, we have proposed a novel architecture which leverages the blockchain technology to enhance the security and privacy of IoT for healthcare applications. In the proposed architecture called smart healthcare system for patients in ICU (SHPI), critical data is processed in edge computing which is located inside the hospital to reduce the communication latency. In order to provide tramper-proof medical records and data confidentiality SHPI uses blockchain technology and cryptographic methods respectively. Also, a data accessing token system is introduced to separate the group of users based on their roles. This system utilizes smart contracts to record every event for providing transparency in medical activities. In order to describe the working principles a logical analysis is carried out, that shows the system is capable of providing the desired security and privacy.

This paper presents a region based blind medical image watermarking (MIW) scheme for ensuring authenticity, integrity and confidentiality of medical images. Medical image is segmented into region of interest(ROI) and region of non interest (RONI). ROI is watermarked for tamper detection and recovery in the spatial domain. For providing confidentiality and authenticity, electronic patient record (EPR) and hospitals logo is embedded as a robust watermark in RONI using IWT-SVD hybrid transform. Various experiments were carried out on different medical imaging modalities for performance evaluation of the proposed scheme in terms of imperceptibility, robustness, tamper detection and recovery. Evaluation results show that the visual quality of watermarked image is good and it is robust under common attacks. A comparison with well known schemes has been performed to show superiority of the proposed method.

This paper proposes a light-tree based heuristic algorithm, called 0/1 knapsack based multicast traffic grooming, in order to minimize the network cost by reducing the number of higher layer electronic and optical devices, such as transmitters, receivers, and splitters, and used wavelengths in the network. The proposed algorithm constructs light-trees or sub light-trees, which satisfy sub bandwidth demands of all multicast requests. We present a light-tree based integer linear programming (ILP) formulation to minimize the network cost. We solve the ILP problem for sample four-node and six-node networks and compare the ILP results with the proposed heuristic algorithm. We observe that the performance of the proposed algorithm is comparable to the ILP in terms of cost. When the introduced ILP is not tractable for large network, the proposed algorithm still able to find the results. Furthermore, we compare the proposed heuristic algorithm to existing heuristic algorithms for different backbone networks. Numerical results indicate that the proposed heuristic algorithm outperforms the conventional algorithms in terms of cost and resource utilization.

The dynamic multicast traffic grooming is an efficient way to minimize the utilization of network resources such as wavelengths, transmitters, receivers and splitters and minimizing the traffic blocking probability. In this article, we initially formulate an Integer Linear Programming for minimizing the blocking probability associated with the network resources, then propose a heuristic algorithm for dynamic multicast traffic grooming problem to achieve our objective. We divide our problem into three sub-problems: (1) routing/provisioning of multicast requests; (2) light-tree based logical topology design, and (3) traffic grooming problem. In this approach, we will decide the appropriate grooming technique based on the ratio of number of wavelengths used in the networks to the number of transceivers and splitters. We use computer simulations to evaluate the performance of various policies used in this algorithm. Our simulation results demonstrate that our approach significantly reduces the blocking probability constraint by the network resources used in the network when compared with the other existing algorithms.

With the development of multimedia services in Internet technology, there comes a big gap between bandwidth utilization and the blocking probability for multicast requests in the optical wavelength division multiplexing (WDM) networks. The objective of the proposed approach is to minimize the number of requests blocked in a dynamic multicast optical networks by minimizing the total resources (such as transceivers, splitters and wavelengths) used by the requests and simultaneously increase the bandwidth utilization. Since there are multiple wavelengths on a WDM optical fiber of fixed capacities, minimizing the number of wavelengths to be used is a variation of the bin packing problem. In the bin packing problem, multicast requests of different granularities or subwavelengths must be packed into a finite number of wavelength channels, in such a fashion that it minimizes the number of wavelengths used. In computational complexity theory, it is a combinatorial NP-hard problem. Therefore, we propose two heuristic approaches that provide the efficient resource utilization. These algorithms are called Multicast Traffic Grooming with Bin packing Best-Fit (MTG-BBF) and Multicast Traffic Grooming with Bin packing First-Fit (MTG-BFF). Both the algorithms are derived from standard Bin pack heuristic approach and we map our problem with such kind of approach. Our simulations demonstrated that both the algorithms significantly reduce the blocking probability (BP) compared to well known existing algorithms and MTG-BFF produces slightly better performance than MTG-BBF in the standard networks.

Network survivability is an important factor in the design of WDM optical networks. In dynamic provisioning context, a typical connection request may require bandwidth less than a wavelength channel capacity and it may also require protection from network failure, typically fiber cuts. In this paper, we investigate the multicast traffic protection with grooming in WDM mesh network under single link failure and propose a novel multicast protection algorithm called shared segment protection with grooming (SSPG) that constructs the primary or working light-tree and corresponding link disjoint backup light-tree for each dynamic multicast connection request. In this approach, a backup segment can efficiently share the wavelength channel capacity of its working segments and simultaneously use the common network resources at the backup light-tree. In order to efficiently utilize the network resources (such as wavelength, transceivers, optical splitters and wavelength channels), the sub-wavelength demands are groomed to protect multicast requests against single link failure. The main objective of this work is to minimize the blocking probability of client calls or bandwidth blocking probability of a dynamically changing multicast request of WDM optical networks and efficiently utilize the network resources, respectively. The performances of various algorithms are evaluated based on extensive simulations to study dynamic provisioning of survivable multicast sessions in standard WDM mesh networks. The simulation results reveal that the proposed SSPG produces better performance in terms of blocking probability (in terms of requests), bandwidth blocking probability (in terms of bandwidth capacity), wavelength channel utilization and cost which is associated with the network resources such as transceivers, splitters and wavelengths than existing standard link shared protection with grooming (LSPG) and path-pair shared protection with grooming (PSPG) algorithms.

In this paper, we have considered an optimal design and provisioning of WDM networks for the grooming of sub-wavelengths traffic requests. As higher layer electronic ports, such as transceivers and optical splitters are dominant cost factors of an optical network, it is essential to reduce their numbers of use while grooming the multicast traffic requests into high bandwidth light-trees. This paper provides an optimal cost design of WDM networks with multicast traffic grooming under static traffic demand. We develop a unified framework for the optimal provisioning of different practical scenarios of multicast traffic grooming in a static traffic scenario. In this study, we design an Integer linear Programming (ILP) formulation for multicast traffic grooming to minimize the cost associated with the higher layer electronic ports such as transceivers, splitters and wavelengths, and simultaneously maximize the bandwidth utilization of the network. We propose a heuristic algorithm called Efficient Light-Tree based Multicast Traffic Grooming (ELT-MTG) algorithm to achieve scalability in large size optical networks. Simulations are conducted on several standard well-known WDM mesh networks to study the design cost (based on number of transceivers, optical splitters and wavelengths) used in the networks. The result, thus obtained by comparison, helped us to conclude that the proposed approach ELT-MTG gives better performance than well-known existing logical-first sequential routing with single-hop grooming (LFSEQSH) and logical-first sequential routing with multi-hop grooming (LFSEQMH) algorithms in a static traffic environment.

Survivability of traffic grooming problem for optical mesh networks is employed in WDM mesh networks. A typical connection request may require bandwidth capacity which is lesser than the wavelength channel capacity of an optical fiber network, and it may also require protection from link failures of the network, typically fiber cut. As higher layer electronic ports, such as transceivers and optical splitters are dominant cost factors of an optical network, it is essential to reduce their number of use when grooming the multicast traffic into high bandwidth light-trees. This paper, provides an near optimal cost design of WDM networks with survivable multicast traffic grooming under static traffic demands. In this paper, we have proposed a heuristic approach called Multicast Traffic Grooming with Survivability (MTGS) at light-tree level for grooming a connection request with segment protection. In this segment protection scheme, backup paths use the network resources (such as transceivers, optical splitters and wavelengths), as long as their working paths are failed simultaneously. In our proposed approach, working paths and backup paths are groomed separately and protecting each specific link when two links failed simultaneously. The main objective of this approach is to minimize the cost of the network which is associated with network resources. We have compared our work with existing approach called logical-first sequential routing with single-hop grooming (LFSEQSH) and logical-first sequential routing with multi-hop grooming (LFSEQMH) algorithms. In the existing multicast traffic algorithms, we add survivability with traffic grooming in static traffic environment. The results, thus obtained by comparison depict that our proposed approach yields better performance in term of network cost than existing algorithms.

In Wavelength Division Multiplexing (WDM) optical networks, the failure of network resources (e.g., fiber link or node) can disrupt the transmission of information to several destination nodes on a light-tree based multicast sessions. Thus, it is essential to protect multicast sessions by reserving resources along back-up trees. So that if primary tree fails to transmit the information back-up tree will forward the message to the desired destinations. In this paper, we address the problem of survivable of multicast routing and wavelength assignment with sub-wavelength traffic demands in a WDM mesh networks. In this work, we extend the approach of segment disjoint protection methodology to groom the multicast sessions in order to protect them from single link failures. We have proposed an efficient approach for protecting multicast sessions named light-tree based shared segment protection grooming (LTSSPG) scheme and compared with existing multicast traffic grooming with segment protection (MTG-SP) approach. In case of MTG-SP, each segment of primary tree is protected by dis-joint segment in the back-up tree to share the edges or segment. Whereas in case of LTSSPG approach, the segment are shared between the primary as well as back-up trees. The main objective of this work is to minimize the cost in terms of number of wavelengths requirement and optical splitters as well as minimizing the blocking probability of network resources. The performance of various algorithms are evaluated based on extensive simulations in standard networks. © 2014 IEEE.

In this paper, we design an optimization problem for grooming of multicast traffic requests in WDM mesh networks. The objective is to minimize the network cost by minimizing the number of optical splitters and at the same time minimizing the total number of wavelengths used in the network. We propose a heuristic algorithm called Priority based Sub-Light Tree Grooming (PSLTG) to achieve scalability for larger networks. PSLTG tries to satisfy all multicast requests by constructing sub-light trees. A mathematical formulation is derived to minimize the network cost associated with the number of optical splitters and number of wavelengths used in the networks. Simulations are conducted on several standard networks to compare the cost and required number of wavelengths used in the networks. The results thus obtained by comparison, helped us to conclude that the proposed approach PSLTG produces better performance than well known Sub-Light Tree Saturated Grooming (SLTSG) and Multicast Traffic Grooming (MTG) algorithms.

With the growing popularity of multicast applications and the recognition of the potential achievable efficiency gain of the traffic grooming, we face the challenge of optimizing the design of WDM optical networks with sparse splitting multicast traffic grooming. Efficiently grooming low speed connections onto a high capacity wavelength channel can significantly improve the bandwidth utilization in an optical network. In this study, we investigate the problem of sub-wavelength traffic grooming in a WDM optical networks and shows how to take the advantages of multicast capable nodes in grooming these sub-wavelength traffic. The problem of constructing optimal multicast routing trees and grooming their traffics in WDM optical mesh networks is NP-hard. Therefore, we propose an heuristic approach to solve the problem in an efficient manner. The main objective of this paper is to maximize the bandwidth utilization and simultaneously minimize the wavelength usage in a sparse splitting optical network. The problem is mathematically formulated. We have simulated the proposed heuristic approach Multicast Sparse Splitting Traffic Grooming (MSSTG) with different network topologies and compared the performance with Multicast Traffic Grooming with Shortest Path (MTG-SP) algorithm. The simulation results shows that the proposed approach produces better result than existing MTG-SP algorithm. © 2013 Springer-Verlag Berlin Heidelberg.

With the advancement of technologies in the field of communication and increasing need of one-to-many communication in different spares of life, multicast communication is becoming a challenging issue in modern communication. The main challenge of multicasting in fiber optics communication is request blocking due to finite resource in WDM optical network. In this paper we have proposed a priority search technique to route multicast sessions in a WDM mesh network. The simulation result shows that the proposed algorithm increases the throughput of communication in a delay constrained application by reducing rate of request blocking in a sparse-split constrained finite wavelength WDM mesh network. © 2012 IEEE.

Multicast Routing and Wavelength Assignment (MRWA) in WDM mesh network is a vital problem in one-to-many communication in photonic domain. The majority of works done in this problem is based on different heuristics. In this paper we have formulated the problem as a delay controlled splitting minimization problem and applied a genetic algorithm to provide a near optimal solution for multicasting in WDM mesh network. The major contribution in this paper is that we have taken multiple objectives in account including QoS parameter like delay and network resource parameters like splitters, optical channels while generating light-tree for each multicast session request. Here we have designed a novel tunable fitness function which provides efficient solution for MRWA problem with multiple conflicting objectives in a constrained setup. The simulation results establish the truth of this claim significantly. © 2012 IEEE.

Multicast Routing and Wavelength Assignment (MRWA) is a technique implemented in WDM optical networks, where dedicated paths are established between a source and a set of destinations, unlike unicasting where a source is connected with only one destination. For a multicast session request a multicast tree is generated to establish a connection from source to all the destinations. A wavelength is assigned to each and every branches of the generated multicast tree to create a light-tree for the session. In this work, we have tried to minimize the wavelength usage to establish multicast sessions for a set of multicast session requests. Our approach is to minimize the size of the multicast tree by sharing branches, as much as possible, to connect all the destinations from the source node. A lesser usage of links minimizes the collision probability for the assignment of wavelength, say w, in each of the selected links to be assigned the wavelength. Secondly, greater sharing implies lesser splitting. As splitters are costly, minimum usage of splitters incurs lesser infrastructure cost in the network. The effectiveness of our approach has been established through extensive simulation on different set of multicast session under different network topologies and comparing with standard Minimal Spanning Tree (MST) based algorithm. The simulation shows our algorithm performs better than the MST based algorithm. © 2011 Springer-Verlag.