Blockchain is a decentralized, public, and distributed ledger designed to securely record and track transactions. It possesses the potential to transform various industries, including healthcare, supply chain management, and financial services, by enhancing transparency, efficiency, and trust. Despite its promise, blockchain development continues to face several challenges, particularly concerning security, scalability, and standardization. This paper provides a comprehensive analysis of blockchain technology, focusing on its quality of service (QoS), security mechanisms, and the latest frameworks and models shaping its evolution. Furthermore, it examines existing limitations and identifies key open research challenges that must be addressed for broader adoption. The findings suggest that blockchain can substantially improve operational efficiency and data integrity across multiple domains; however, realizing its full potential requires continued research and technological advancement to overcome current barriers.

Cloud computing provides flexible and on-demand access to computing and storage resources over the Internet. While cloud services offer multiple free and paid storage options, users are often unaware of their actual efficiency when accessing them through broadband and mobile data networks. This paper presents a Quality of Experience (QoE) assessment of four popular free cloud storage services - Alibaba Cloud, JioCloud, JazzDrive, and Dropbox - evaluated using both broadband and mobile networks. Using subjective user feedback and network performance metrics such as upload/download speed and jitter, we analyze user satisfaction when uploading and downloading different file types. The results reveal that Dropbox and JioCloud generally offer better QoE under broadband, while Alibaba Cloud performs consistently well under mobile data. These insights can help users, service providers, and smart city planners optimize cloud-based storage and data-sharing performance for improved digital experiences.

One of the emerging paradigms in the diagnosis and severity assessment of skin disorders, particularly acne, on the face is the use of advanced digital technology (ADT) in skin disease monitoring. It is said to be a very prevalent issue that has to be looked at by experts in this day and age. Nonetheless, the traditional approach to acne diagnosis still relies on the opinions and expertise of medical professionals. There could be fatal outcomes from both delayed and inaccurate diagnoses. Since acne is a condition that directly affects the healthcare system, this study focusses on accelerating the diagnostic process and closing the gap between diagnosis and treatment. In this work, we introduce a smart face acne disease level monitoring device that allows acne sufferers in different geographical locations to track the severity and specifics of their acne and to communicate precautions. Convolutional neural networks, or CNNs, play a major role in this suggested architecture's AI-enabled cloud-based IoT device interconnectivity. Based on a set of photos, CNNs predict the degree of face acne, which could have implications for further study. This proposed study also addresses the influence of age. Geographically speaking, the architecture provides all the domains of skin diagnostic and preventive scheme, notably for acne diagnosis, addressing the present issue faced by patients with limited or no access to e-healthcare services.

With the robust development of artificial intelligence (AI), especially image processing has made information technology more efficient and effective in the sense to evaluate facial features, even though there has been masked on the face. However, the accuracy of acne detection and related severity analysis is becoming a significant prospect for the precise treatment of patients. Due to this, close severity is one of the features that need to be added first, while it is considered a highly challenging aspect for dermatologists because the similar appearance of acne in the face reduces the rate of accuracy when examining. It poses a serious problem in the domain of biomedical processing and controls. In this paper, we contribute to four different folds. Initially, this paper presents a novel framework that provides a platform in order to measure localization and segmentation. In this process, consultants receive better accuracy and efficiency during the process of acne detection and severity analysis. Second, this paper utilizes deep neural networks (DNNs) as a backend process to lightning the extraction of multi-scale features through a multi-hierarchy neural net for regionalized facial features to investigate distinction and localization. Third, a class-based segmentation approach customizes and integrates with the proposed framework to examine the background and facial skin separation to distinguish different classes to obtain severity marking. Fourth, the facial skin segmentation classes are built as a cluster segment using generative adversarial network (GAN). With a performance rate of 3.112% (accuracy), 1.131% (segmentation), 2.317% (localization), and 1.573% (GAN-based high-resolution network management), respectively, the suggested framework also shows good results in severity analysis for acne detection, which is comparable to or better than the previously published papers. However, the simulation shows that the technological collaboration achieves promising results and elaborates on the accuracy and efficiency while the detection of acne as compared to other state-of-the-art baseline methods.

Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.

The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.

Abstract Information hiding aims to embed a crucial amount of confidential data records into the multimedia, such as text, audio, static and dynamic image, and video. Image‐based information hiding has been a significantly important topic for digital forensics. Here, active image deep steganographic approaches have come forward for hiding data. The least significant bit (LSB) steganography approach is proposed to conceal a secret message into the original image. First, the lightweight stream encryption cryptography encrypts secret information in the cover image to protect embedded information from source to destination. Whereas the encrypted embedded cover information into the carrier of stego‐image with the help of the LSB and then transmit. In the proposed investigational scheme, a convolutional neural net is used. A model is trained to detect and extract patterns of image hidden features, encrypted stego‐image optimization, and classify original and cover images of steganography. Through the experiment result on the forensic image database for mobile steganography of the Center for Statistics and Application in Forensic Evidence, the overall embedded and extracting that the proposed scheme can achieve information hiding as well as revealing with an accuracy rate of 95.1%. The experimental result shows the robustness of the model in terms of efficiency as compared to other state‐of‐the‐art schemes.