The increased adoption of machine learning techniques has led to exponential growth in data generation and utilization. This growth has necessitated efficient storage, processing, and utilization of this data, which presents critical challenges, particularly in resource-constrained environments such as the Internet of Things (IoT) and edge devices. Data distillation has emerged as a promising solution that reduces dataset size while preserving essential information and optimizing computational resources. This survey provides a comprehensive analysis of data reduction techniques, covering methodologies such as knowledge distillation, coreset selection, hyperparameter optimization, and generative modeling. We further explore various data distillation learning frameworks, including performance, gradient, parameter, and distribution matching, highlighting their effectiveness in different data modalities such as images, graphs, and text. Furthermore, we examine the implications of data distillation in key areas such as continual and federated learning, privacy preservation, security, healthcare, IoT applications, and edge computing. By enabling lightweight models with minimal computational overhead, data distillation facilitates real-time inference and decision-making on edge devices, making it highly relevant for low-power, bandwidth-limited environments. Data distillation offers numerous advantages in improving model efficiency, reducing training costs, and enhancing privacy. However, data distillation faces numerous challenges related to scalability, computational complexity, and information retention. This survey identifies these challenges and outlines potential future research directions, providing insights for researchers seeking to leverage data distillation for scalable and efficient machine-learning applications.

Data privacy is a critical concern in the digital age. This problem has compounded with the evolution and increased adoption of machine learning (ML), which has necessitated balancing the security of sensitive information with model utility. Traditional data privacy techniques, such as differential privacy and anonymization, focus on protecting data at rest and in transit but often fail to maintain high utility for machine learning models due to their impact on data accuracy. In this article, we explore the use of synthetic data as a privacy-preserving method that can effectively balance data privacy and utility. Synthetic data is generated to replicate the statistical properties of the original dataset while obscuring identifying details, offering enhanced privacy guarantees. We evaluate the performance of synthetic data against differentially private and anonymized data in terms of prediction accuracy across various settings—different learning rates, network architectures, and datasets from various domains. Our findings demonstrate that synthetic data maintains higher utility (prediction accuracy) than differentially private and anonymized data. The study underscores the potential of synthetic data as a robust privacy-enhancing technology (PET) capable of preserving both privacy and data utility in machine learning environments.

Historical patients’ medical data has an important impact on the healthcare industry for providing the best care to patients through intelligent health diagnosis and prediction of diseases. The existing intelligent health diagnosis systems collect data from medical institutions or laboratories and then use machine learning algorithms to predict diseases. But, in most cases, the medical institutions have incomplete medical data of the patients since a patient may consult different specialists (from various hospitals) during the treatment process. To overcome this problem, we build a smart and secure federated learning framework for intelligent health diagnosis with a blockchain-based incentive mechanism and nonfungible tokens (NFTs)-based marketplace. We make use of NFTs to develop clear demarkations on the ownership and accessibility of the data of patients. We create an NFT marketplace that manages access to the historical medical data of patients. A comprehensive incentive mechanism based on several factors, including the quality and relevance of the data, the frequency, regularity of data uploading, etc., is incorporated to encourage and penalize the patients based on their contributions to the global model. We used the Polyak-averaging technique for aggregating local models to form a global model. The extensive analysis shows that the proposed model achieves comparable performance with the centralized machine learning models while affording better security and access to better data. The results also show the efficacy of the proposed blockchain-based incentive mechanism.

Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset:https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.

Abstract The prediction of news popularity is having substantial importance for the digital advertisement community in terms of selecting and engaging users. Traditional approaches are based on empirical data collected through surveys and applied statistical measures to prove a hypothesis. However, predicting news popularity based on statistical measures applied to past data is highly questionable. Therefore, in this article, we predict news popularity using machine learning classification models and deep residual neural network models. Articles are usually made up of textual content and in many cases, images are also used. Although it is evident that the appropriate amount of textual data is required to extract features and create models, image data is also helpful in gaining useful information. In this article, we present a novel multimodal online news popularity prediction model based on ensemble learning. This research work acts as a guide for extensive feature engineering, feature extraction, feature selection and effective modelling to create a robust news popularity Prediction Model. Three kinds of features—meta‐features, text features and image features are used to design an influential and robust model. The relative error performance measure Root Mean Squared logarithmic error (RMSLE) is used to quantify the popularity prediction error. Further, the RMSLE outcome shows 0.351 which is the lowest error value given by the proposed model. Further, the most important features are also sought out to show the dependence of the best‐fit model on text and image features.

Abstract Online social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, which pertains to finding methods for maximizing the spread of information (influence) across an OSN. Existing works in IM widely use a pre‐defined edge propagation probability for node activation. Hurst exponent (H), which depicts the self‐similarity in the time series depicting a user's past interaction behaviour, has also been used as activation criteria. In this work, we propose a Time Series Characteristic based Hurst‐based Diffusion Model (TSC‐HDM), which calculates H based on the stationary or non‐stationary characteristic of the time series. TSC‐HDM selects a handful of seed nodes and activates a seed node's inactive successor only if H > 0.5. The proposed model has been tested on four real‐world OSN datasets. The results have been compared against four other IM models – Independent Cascade, Weighted Cascade, Trivalency, and Hurst‐based Influence Maximization. TSC‐HDM is found to have achieved as much as 590% higher expected influence spread as compared to the other models. Moreover, TSC‐HDM has attained 344% better average influence spread than other state‐of‐the‐art models namely LIR, A‐Greedy, LPIMA, Genetic Algorithm with Dynamic Probabilities, NeighborsRemove, DegreeDecrease, IGIM, IRR, and PHG.

This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

The rapid improvement in the global connectivity standards has escalated the level of trade taking place among different parties. Advanced communication standards are allowing the trade of all types of commodities and services. Furthermore, the goods and services developed in a particular region are transcending boundaries to enter into foreign markets. Supply chains play an essential role in the trade of these goods. To be able to realize a connected world with no boundary restrictions in terms of goods and services, it is imperative to keep the associated supply chains transparent, secure, and trustworthy. Therefore, some fundamental changes in the current supply chain architecture are essential to achieve a secure trade environment. This article discusses the supply chain's security-critical application areas and presents a detailed survey of the security issues in the existing supply chain architecture. Various emerging technologies, such as blockchain, machine learning (ML), and physically unclonable functions (PUFs) as solutions to the vulnerabilities in the existing infrastructure of the supply chain have also been discussed. Recent studies reviewed in this work reveal a growing sentiment in the industry toward new and emerging technologies, such as Internet of Things (IoT), blockchain, and ML. While many organizations have already adopted IoT applications and artificial intelligence systems in their businesses, widespread adoption of blockchain remains distant. It has also been found that over the past decade, PUF-based authentication systems have gained much ground. However, a proper reference model for their implementation in complex supply chains is still missing.

Governments and public sector entities around the world are actively exploring new ways to keep up with technological advancements to achieve smart governance, work efficiency, and cost optimization. Blockchain technology is an example of such technology that has been attracting the attention of Governments across the globe in recent years. Enhanced security, improved traceability, and lowest cost infrastructure empower the blockchain to penetrate various domains. Generally, governments release tenders to some third-party organizations for different projects. During this process, different competitors try to eavesdrop the tender values of others to win the tender. The corrupt government officials also charge high bribe to pass the tender in favor of some particular third party. In this article, we presented a secure and transparent framework for government tenders using blockchain. Blockchain is used as a secure and immutable data structure to store the government records that are highly susceptible to tampering. This work aims to create a transparent and secure edge computing infrastructure for the workflow in government tenders to implement government schemes and policies by limiting human supervision to the minimal.

Summary The emergence of mobile cloud computing enables mobile users to offload computation tasks to other resource‐rich mobile devices to reduce energy consumption and enhance performance. A direct peer‐to‐peer connection among mobile devices to offload computation tasks can be a highly promising solution to provide a fast mechanism, especially for deadline‐sensitive offloading tasks. The generic blockchain‐based system might fail in such a scenario due to it being a heavyweight mechanism requiring high power consumption in the mining process. To address these issues, in this article, we propose a directed acyclic graph‐enabled mobile offloading (DAGMO) algorithm. DAGMO model is empowered by traditional blockchain features and provides additional advantages to overcome the fundamental limitations of generic blockchain. A game‐theoretic approach is used to model the interactions between mobile devices. The numerical analysis proves the proposed model to enhance the overall welfare of the participating nodes in terms of computation cost and time.

The exponential surge in the number of vehicles on the road has aggravated the traffic congestion problem across the globe. Several attempts have been made over the years to predict the traffic scenario accurately and consequently avoiding further congestion. Crowdsourcing has come forward as one of the most adopted methods for predicting traffic intensity using live data. However, the privacy concerns and the lack of motivation for the live users to help in the traffic prediction process have rendered existing crowdsourcing models inefficient. Towards this end, we present an advanced blockchain-based secure crowdsourcing model. Not only does our model ensure privacy preservation of the users, but by incorporating a revenue model, it also provides them with an incentive to participate in the traffic prediction process willingly. For accurate and efficient traffic jam probability estimation, our work proposes a neural network-based smart contract to be deployed onto the blockchain network. The results reveal that the proposed model is highly efficient in terms of attaining high participation and consequently obtaining highly accurate predictions.

Vehicular Ad-hoc Network (VANET) poses to be a promising technology for the future since it increases the comfort level of the drivers while also enhancing the safety measures for them. The main aim of VANETs is to enable communication among vehicles and roadside units (RSUs) using vehicle-to-vehicle (V2V) and vehicle-to-RSU (V2R) networks. VANET applications have a vast potential for growth owing to the increasing number of smart cities around the globe and advancement taking place in the technology sector. However, with all their benefits, VANETs also face several security challenges. The sensitive nature of data being transferred turns VANETs prone to malicious attacks. To overcome the security challenges, this paper proposes a distributed Directed Acyclic Graph (DAG) enabled vehicular network comprising several requesting vehicles and RSUs. The proposed model is based on advanced blockchain and therefore provides a strong level of security and data immutability. Furthermore, the interactions between the requesting vehicles and the RSUs have been modeled using an auction-based game-theoretic smart contract deployed on the blockchain.

The rapid increase in the number of mobile devices across the globe has brought a new challenge to the forefront, one of mobile traffic management. The ever-increasing number of mobile devices leads to the generation of a large amount of data and computationally intensive applications, which contributes heavily to cellular network congestion. To solve this issue, we propose a mobile data offloading scheme based on a distributed ledger technology (DLT). Existing mobile data offloading schemes based on DLT employ conventional blockchain to set up a peer-to-peer (P2P) network of mobile users. Although these schemes have gained ground in improving the Quality of Experience (QoE) for end-users, they lack efficiency and scalability. Furthermore, generic blockchain does not provide timestamp ordering of events, which is necessary to ensure the computation of delay-sensitive tasks. To overcome these challenges, we propose the use of a directed acyclic graph (DAG) data structure for mobile data offloading. Finally, to ensure time and cost optimality, a game-theoretic approach has been proposed in this paper.

The rapidly increasing mobile traffic across the globe has proliferated the deployment of cellular base stations, which has, in turn, led to an increase in the power consumption and carbon footprint of the telecommunications industry. In recent times, solar-powered base stations (SPBSs) have gained much popularity in the telecom sector due to their ability to make operations more sustainable. However, some potential energy benefits rendered by the SPBSs have not yet been realized. In areas with dense base station deployment or low mobile traffic, SPBSs store surplus energy, which, in most instances, gets lost due to limited charge storage capacity of the batteries. To limit the wastage of energy, an appropriate mechanism enabling the utilization of excess energy produced by these base stations can be adopted. To this end, we model a Base Station-to-Grid (BS2G) network in which the grid can utilize surplus energy spared by the SPBSs. To overcome challenges in regards to scalability, robustness, and cost-optimization, we propose using the blockchain technology to create the BS2G network. Blockchain is a distributed ledger designed to record transactions in a transparent, lightweight, and tamper-proof manner. To make energy trade between base stations and the grid cost-effective, a game-theoretical approach has also been adopted in this paper. The proposed model simplifies the process of energy trading while also making it cost-optimal.

Stock exchanges around the world are exploring the best possible solution that can improve trading efficiency, lower the risks and tighten secu- rity levels. The working and functioning of a stock exchange involves very hectic and cumbersome pro- cedures which are time consuming, cost inefficient and can be prone to numerous risks. Machine learning and Blockchain are most popular upcoming technologies. In this paper we present a novel secure and de- centralized intelligent stock market prediction model. We present a blockchain based solution for stock exchange model that uses machine learning accessible smart contracts. The machine learning model makes a prediction on the future of the stock market providing an intelligent solution for secure stock market.