Fusion: Practice and Applications

In this study, we present a multi-level fusion of deep learning technique for facial expression identification, with applications spanning the fields of cognitive science, personality development, and the detection and diagnosis of mental health disorders in humans. The suggested approach, named Deep Learning aided Hybridized Face Expression Recognition system (DLFERS), classifies human behavior from a single image frame through the use of feature extraction and a support vector machine. An information classification algorithm is incorporated into the methodology to generate a new fused image consisting of two integrated blocks of eyes and mouth, which are very sensitive to changes in human expression and relevant for interpreting emotional expressions. The Transformation of Invariant Structural Features (TISF) and the Transformation of Invariant Powerful Movement (TIPM) are utilized to extract features in the suggested method's Storage Pack of Features (SPOF). Multiple datasets are used to compare the effectiveness of different neural network algorithms for learning facial expressions. The study's major findings show that the suggested DLFERS approach achieves an overall classification accuracy of 93.96 percent and successfully displays a user's genuine emotions during common computer-based tasks.

the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-level/hybrid level fusion system architectures can effectively integrate data/images from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.

The paper proposes a method for improving the accuracy of image classification by combining CNNs and the Binary Marine Predators Algorithm (BMPA). The CNNs used in the study, ResNet 50 and AlexNet, were trained on ImageNet and used to extract features from the images in the dataset. Features are taken from layers (avg_pool) in ResNet 50 and (drop7) in AlexNet. These features were then fed into the BMPA algorithm, which selected the most relevant features and removed irrelevant ones to improve the classification process. The proposed method is said to be efficient, capable of achieving higher classification accuracy, and able to select the best features. The authors believe that this approach could be applied to a variety of other image classification tasks. It is important to note that the effectiveness of this method should be evaluated on a range of datasets and compared to other state-of-the-art methods.

In this paper, the researcher discussed a developed approach to the detection of outliers that is suited to multivariate data fusion. The challenge in outlier detection when dealing with multivariate data it is the detection of the outlier with more than two dimensions. To address this issue, the researcher developed a method to detect anomalies using methods based on local density including comparing a specific observations density with the densities of its neighboring observations. To make such comparisons, the researcher often employs an outlier score. In this study, various density estimation functions and distance metrics were utilized. Nadaraya-Watson kernel regression for multivariate data considered the KNN with multivariate data. Finally, the estimate of the Volcano kernel method is an essential method for outliers detection. In the simulation experiments of multivariate data with (4,6,8) variables and (60,120,180) observations, the results of simulation experiments by using the criterion of the precision evaluation showed that the N-W method is better than the VOL method in outlier detection in multivariate data.

The objective of this paper is to build the neutrosophic version of the RSA crypto-algorithm, where we use the foundations of fusion neutrosophic number theory such as neutrosophic phi-Euler's function, neutrosophic congruencies, and neutrosophic inverses to build novel algorithms for cryptography depending of famous RSA algorithm.

Non-Fungible Tokens (NFTs) are one-of-a-kind digital items with static or continuous visual and audio content. NFTs digitally represent any assets that may hold photos, gifs, audio, videos, or any other data-based storable material. These assets may come under a variety of asset groups, including art, in-game goods, and entertainment collecting units. What makes them appealing is their exclusivity, in the sense that each NFT is unique to itself, and ownership is determined by a digital certificate. In the first half of 2021, NFT sales totaled more than a billion. The NFT Software as a service (SAAS) based system is a one-of-a-kind offering and concept for thinking outside the box and presenting intellectuals and creative treasures and exhibiting these objects to ensure the security and integrity of digital assets. The existence of core decentralized networks allows for unrestricted access to this material as well as further analysis. Based on the Web3 Blockchain technology, these assets may be traded and represent next-generation ownership.  In this paper, Adaptive Improved Convolutional Neural Networks (AICNN) are used to forecast NFT to provide a SAAS NFT collector. We also introduce Tree-seed Chaotic Atom Search Optimization (TSC-ASO) algorithm to optimize the forecasting process. The proposed method of NFT price forecasting is evaluated and compared with the existing forecasting methods. To produce an accurate report for NFT price forecasting, the proposed method will be effective.

The demand for cloud computing has increased immensely, and its security is becoming challenging. The enormous growth in cloud computing adaptation has been observed, but the information security concerns have not been addressed thoroughly. The security issues related to cloud computing are a concern. The emergence of Blockchain as a key security provider has increased the hope for the availability of a secure cloud computing environment. The data-sharing technique based on the cloud scenario relies on the network's storage and architecture; however, the storage providers are considered trusted third parties for data-sharing and storage purposes. The associated limitations such as security, high operational cost, centralized storage capability, and data availability have become a challenging task, which leads to the development of a trusted data management system for secure data sharing through the Blockchain. This study presents an analysis of secure data-sharing techniques using Blockchain. The related research articles were elicited from several sources such as Springer, IEEE, Elsevier, and other online sources. The primary studies have been categorized into four types: healthcare data sharing, vehicular communication-based data sharing, IoT-based data sharing, and other miscellaneous techniques. The techniques have been analyzed based on various performance metrics. The analysis and findings of this study can pave a way for the future development of safe data-sharing techniques using Blockchain technology.

The objective of this paper is to introduce a novel security model for the encryption of uncertain rational data units represented as single-valued rational neutrosophic numbers by combining refined neutrosophic number theoretical concepts with the El Gamal public key crypto scheme. In addition, some applications on uncertain data units will be shown and illustrated.

The COVID-19 pandemic had a significant impact on many areas of the workforce, including internship programs. The education sector in general started integrating technology as a kind of fusion of technology in different ways.   The internship course was one of the important educational programs that get affected by the pandemic and there was a deep need for technology fusion to overcome the hard times. This study investigates the effects of COVID-19 on internship programs and provides recommendations for implementing effective strategies in a post-COVID-19 World.  To accomplish this, a comprehensive review of relevant literature review has been conducted, including academic journals, governments, and industry reports, as well as employing both qualitative and quantitative research methods. This study also discussed a case study of the Professional Work Shadow Program, an internship for media students specialized in Integrated marketing communication, Broadcasting, International relations, and public relations. The study findings recommend various marketing strategies, which can help media internship providers and beyond offer effective and sustainable programs to university students.

Biometrics provides better authentication. Unprotected biometrics is open to attacks from intruders as stolen biometrics may not be revocable. Although there are several points where attacks can be launched on biometric systems, template databases are said to be the most frequently attacked. When a template database is attacked, attackers can add fresh templates, modify the existing ones, copy or steal templates and later construct a spoof from it or replay it back into the biometric system to impersonate a genuine user. Several template security systems have been presented in the literature to secure biometric templates. Fuzzy vault, as proposed by many researchers is, to some extent, one of the best algorithms to achieve template protection as it has good security. Fuzzy vault, however, lacks irreversibility, revocability, and diversity. To address these disadvantages and strengthen fuzzy vault, this study combines a noninvertible feature transformation template protection algorithm known as cuckoo hashing that possesses irreversibility, revocability, and diversity properties with a fuzzy vault for privacy. The study used fingerprint biometrics as it is widely used. The proposed algorithm was implemented in the MATLAB 2016a environment using FVC 2004 DB1 fingerprint public database. The proposed algorithm recorded a FAR of 0.01% and an FRR value of 0.09% with an EER of 0.05%.