The heart plays a vital role in living organisms. Heart-related disorders are more difficult to diagnose and predict, and errors in diagnosis and prognosis can have fatal consequences. A subfield of artificial intelligence, machine learning uses training from natural occurrences to predict any type of event with minimal support. In this work, we estimate the predictive power of machine learning algorithms that use several feature selection strategies to forecast heart disease. These algorithms include Select k best, Mutual Information Score, Recursive Feature Elimination (RFE), Support Vector Machine (SVM) estimator, and cross-validation. Random forest and gradient boosting were the predictive models used on the Framingham dataset for training and testing. This study uses the SMOTE technique for class imbalance problems. In our research, we got the highest accuracy for select k best feature selection modeled using random forest with an accuracy of 95.61% and gradient boosting with an accuracy of 95.43% considering hyperparameters

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 ALL-Net, 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-ALL-using-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)

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

Diabetes is a prevalent and enduring condition impacting millions of individuals worldwide this is why early detection is essential for efficient management and intervention. The objective of this article is to build a trustworthy diabetes prediction model using the RF-SVM algorithm and employing ensemble techniques, specifically the stacking technique. The Pima Indians dataset, renowned for its comprehensive clinical and demographic features, is utilized for this study. Our proposed RF-SVM ensemble model trains on a subset of the dataset using stratified cross-validation to increase robustness and generalizability. With an accuracy of 86%, the proposed model successfully predicts diabetes, demonstrating its value in early diagnosis and timely treatment. Feature importance analysis can help us better understand the variables that affect how diabetes develops. This study demonstrates the utility of the RF-SVM ensemble model with the stacking technique for diabetes prediction. The developed approach is effective in identifying patients who are at risk, which improves patient outcomes. Future research initiatives may include merging more datasets and researching advanced machine learning approaches to increase prediction accuracy and increase the model’s utility

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.

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.

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. © 2024, Emerald Publishing Limited.

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. © 2024 IEEE.

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.

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.

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.

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.

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.

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.

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(n).

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.

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 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 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.

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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.

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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.

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

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.

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.

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.

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.

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.

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 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.