Early detection is critical to effectively and efficiently managing chronic obstructive pulmonary disease (COPD) and improving patient outcomes. To enhance early COPD detection, this article presents a new multimodal neuro-fuzzy framework called “FuzzyGuard.” FuzzyGuard uses ensemble learning on various datasets, such as computed tomography (CT) scans and audio recordings of coughs and lung sounds, to guarantee comprehensive analysis and accurate diagnosis. The neuro-fuzzy random vector functional link (RVFL) is used in FuzzyGuard for clinical relevance evaluation and early COPD prediction. FuzzyGuard flexibility is increased by using RVFL to pick hyperparameter tuning parameters, including learning rate$(\eta)$, momentum$(\mu)$, the number of epochs, and regularization coefficient. Using ensemble deep learning techniques, the FuzzyGuard-based framework extracts discriminating features from the chest diagnostic images and sputum samples from cough, as well as lung sound samples for COPD classification using an RVFL network, initialized with a random vector. FuzzyGuard’s excellent accuracy is demonstrated by the assessment, which yielded rates of 99.97% (CT-scan model), 96.98% (cough-based model), and 98.65% (output of FuzzyGuard) for early diagnosis of COPD based on early weighted sum-fusion approaches applied to cough and chest X-ray data. FuzzyGuard outperforms established benchmarks, marking a substantial leap in the diagnosis of COPD, and offers better patient treatment and respiratory health outcomes.

Recently, the field of healthcare has experienced remarkable technological advancements. However, a considerable challenge persists in providing state-of-the-art healthcare services to individuals in tribal and remote areas. To solve health issues in remote areas, this article introduces a federated learning framework for the early diagnosis of multivariate pulmonary diseases based on cough (voice) samples collected from tribal regions. The proposed framework performs model training on connected local devices, including those living in remote or tribal regions with limited connectivity to centralized servers. The proposed framework ensures the early diagnosis of multivariate lung diseases, such as chronic obstructive pulmonary disease (COPD), to obtain accurate prediction using ensemble learning techniques. The framework applies a convolutional neural network (CNN) to extract discriminatory features from generated spectrograms of cough (voice) samples to classify COPD and non-COPD (healthy) using transfer learning techniques. The proposed framework demonstrates the efficacy of our proposed framework in minimizing resource utilization and model complexity, achieving an impressive accuracy of up to 98.62% with reduced communication rounds and latency, thus facilitating the early diagnosis of COPD. The prototype model of the proposed framework offers an alternative way of a fast diagnosis of respiratory diseases based on cough samples collected from tribal people, which is used with the support of pulmonary and tuberculosis experts (doctors and professionals) for statistical analysis of critical cases of COPD for early diagnosis.

The capacity of Dataspace enables the distribution of heterogeneous data from several sources and domains and has attracted attention for resolving data integration challenges. Drone data sharing faces challenges such as protecting privacy and security, building trust and dependability, controlling latency and scalability, facilitating real-time data processing, and preserving the caliber of shared models. Therefore, sixth-generation (6G) networks provide high throughput and low latency to improve drone operations; security issues are exacerbated by the sensitive nature of shared data and the lack of centralized monitoring. To address the challenges, this paper presents a conceptual framework for a Dataspace in the Sky to enable secure and efficient drone data-sharing within 6G networks in the transition from Industry 4.0 to Industry 5.0 . The Dataspace in the Sky integrates Federated Learning (FL), a decentralized Machine Learning (ML) approach that enhances security and privacy by sharing models instead of raw data, facilitating effective drone collaboration. However, the quality of shared local models often suffers due to inconsistent data contributions and unreliable recording mechanisms, which can undermine the performance of FL. To tackle the challenges, the framework employs blockchain (BC) to decentralize and secure the Dataspace, ensuring the integrity of contribution records and improving the reliability of shared models. Dataspace in the Sky empowered decentralized data sharing which addresses latency issues by decentralizing decision-making and enhances trust and reliability by leveraging immutable and transparent BC mechanisms. The robustness of Dataspace in the Sky solution is not only secures drone-sharing operations in 6G environments but enables the development of citizen-friendly mobility services, expanding opportunities across smart environments.

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy-limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand to be deployed in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry about WPT technologies and wireless charging scheduling algorithms. Therefore, in this paper, we review the most recent studies related to WPT, including classifications, advantages, disadvantages, and main domains of application. Furthermore, we review the recently designed wireless charging scheduling algorithms (schemes) for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections.

Our study focuses on detecting confusion from EEG data in higher education. Using BiLSTM models, we enhance EEG analysis efficiency and precision. Starting with extensive feature extraction and preprocessing, we improve data quality. We then apply diverse deep learning methods, including BiLSTM models, to predict perplexity-associated EEG signals. Unique data augmentation techniques like random noise injection and synthetic data synthesis bolster model resilience and generalization. Rigorously evaluating our approach with real-world EEG datasets, we achieve a significant accuracy improvement of 99.89%. The evaluation includes analyzing the confusion matrix and classification report, validating our methods' efficacy. Our research offers a comprehensive framework with state-of-the-art deep learning algorithms, particularly BiLSTM models, advancing EEG pattern identification and classification in educational contexts.

Congestive Heart Failure (CHF) is a prevalent cardiovascular disorder that requires early detection for effective management. This paper presents a comprehensive framework leveraging Bidirectional Long Short-Term Memory (BiLSTM) networks for the early detection of CHF using Electrocardiogram (ECG) data. The methodology involves preprocessing ECG signals, extracting relevant features, normalizing the data, and training a BiLSTM model. Our experimental results demonstrate high accuracy and robustness, highlighting the potential of BiLSTM networks in clinical applications for CHF prediction. The proposed method achieves an accuracy of 98.50%, sensitivity of 98.36%, and specificity of 98.57%, outperforming existing techniques. The model's ability to capture temporal dependencies in ECG signals through bidirectional learning contributes to its superior performance in identifying early signs of CHF. The findings suggest that BiLSTM networks hold promise for enhancing CHF detection accuracy, which could significantly impact healthcare outcomes by enabling early intervention and reducing medical costs.

Diabetic Retinopathy (DR) is a serious eye condition that impacts individuals with diabetes, leading to retinal damage and potentially resulting in gradual vision loss. In this paper, we proposed a novel framework for diabetic retinopathy detection using deep learning techniques. The proposed framework employs an ensemble learning model named WENN-4 to classify fundus images into five classes and measure the severity of blindness of individuals. The proposed framework consists of following steps: customized preprocessing method that includes Gaussian Blurring and augmentation techniques, subsequently followed by inputting the processed images into diverse CNN architectures to extract discriminatory feature vectors for classification and early diagnoses and categorizes blindness severity levels, including no-DR, mild DR, severe, moderate, or Proliferative Diabetic Retinopathy (PDR). To enhance the overall performance of our proposed framework, we utilized an ensemble of models, WENN-4. This ensemble comprises ResNet-50, DenseNet-121, InceptionV3, and EfficientNetV2. This model achieves a remarkable accuracy of 93.7% accuracy on test data of APTOS 2019, surpassing existing methods. This ensemble of models lead to increased accuracy by leveraging complementary strengths, correcting individual misclassifications, and ultimately producing superior outcomes.

The dramatic recent increase of the smart Internet of Everything (IoE) in Industry 4.0 has significantly\nincreased energy consumption, carbon emissions, and global warming. IoE applications in Industry\n4.0 face many challenges, including energy efficiency, heterogeneity, security, interoperability, and\ncentralization. Therefore, Industry 4.0 in Beyond the Sixth-Generation (6G) networks demands moving\nto sustainable, green IoE and identifying efficient and emerging technologies to overcome sustainability\nchallenges. Many advanced technologies and strategies efficiently solve issues by enhancing\nconnectivity, interoperability, security, decentralization, and reliability. Greening IoE is a promising\napproach that focuses on improving energy efficiency, providing a high Quality of Service (QoS), and\nreducing carbon emissions to enhance the quality of life at a low cost. This survey provides a comprehensive\noverview of how advanced technologies can contribute to green IoE in the 6G network of\nIndustry 4.0 applications. This survey provides a comprehensive overview of advanced technologies,\nincluding Blockchain, Digital Twins (DTs), Unmanned Aerial Vehicles (UAVs, a.k.a. drones), and\nMachine Learning (ML), to improve connectivity, QoS, and energy efficiency for green IoE in 6G\nnetworks. We evaluate the capability of each technology in greening IoE in Industry 4.0 applications\nand analyze the challenges and opportunities to make IoE greener using the discussed technologies.

In the era of data-driven healthcare, the amalgamation of blockchain and Federated Learning (FL) introduces a paradigm shift towards secure, collaborative, and patient-centric data-sharing. This paper pioneers the exploration of the conceptual framework and technical synergy of FL and blockchain for decentralized data-sharing, aiming to strike a balance between data utility and privacy. FL, a decentralized machine learning paradigm, enables collaborative AI model training across multiple healthcare institutions without sharing raw patient data. Combined with blockchain, a transparent and immutable ledger, it establishes an ecosystem fostering trust, security, and data integrity. The paper elucidates the technical foundations of FL and blockchain, unravelling their roles in reshaping healthcare data-sharing. The paper vividly illustrates the potential impact of this fusion on patient care. The proposed approach preserves patient privacy while granting healthcare providers and researchers access to diversified datasets, ultimately leading to more accurate models and improved diagnoses. The findings underscore the potential acceleration of medical research, improved treatment outcomes, and patient empowerment through data ownership. The synergy of FL and blockchain envisions a healthcare ecosystem that prioritizes individual privacy and propels advancements in medical science.

The Metaverse is an emerging concept that aims to integrate the physical and virtual worlds, creating a shared 3D virtual world where users can interact and immerse in new experiences. With the rise of Metaverse-driven Cyber-Physical-Social Systems (CPSSs), integrating drones as a critical technology in the Metaverse has become increasingly important. CPSSs have become proliferating and integral to our daily lives. This paper proposes a conceptual framework for Metaverse-driven drone edge intelligence, which integrates drone-enabled sensing, communication, and computation to enable real-time decision-making in CPSSs. We present a detailed analysis of the challenges and opportunities for integrating drones in the Metaverse and discuss the potential impact of our framework on various application domains. Our work contributes to advancing the Metaverse and CPSSs by providing a novel approach for empowering real-time decision-making and enabling new user experiences through integrating drones and the Metaverse. The proposed framework has the potential to revolutionize the way we approach data-driven decision-making in various industries and applications, including precision agriculture, transportation, emergency response, smart cities, healthcare, manufacturing, and energy.

Parkinson's disease (PD) is a neurological condition that is on the rise and disrupts the nervous system. However, there is no specific diagnosis for Parkinson's disease; only a variety of motor signs can be used to identify it. A speech impairment was found in more than 90% of PD patients. This study presents a voice and speech signal data-based model for PD identification. The PD is the speech data set used in this experiment has a great amount of dimension with very few data points. Different data pretreatment techniques, such as data standardization, mul-ticollinearity diagnostic, and dimensionality reduction approach, were used in our suggested model to enhance the quality of the data. Different Machine Learning (ML) classifiers were employed to categorize PD, including k-nearest Neighbor, Support Vector Machine, Random Forest, AdaBoost, and Logistic Regression. In this experiment, grid search, cross-fold validation, and hyper-parameter tweaking were used to optimize classifier performance and maintain the class distribution of the unbalanced data set. Our suggested model outperformed the prior tests on the same data set by around 98.00% and reached a maximum accuracy of 98.10

Summary Lightweight cryptography is a rapidly developing research field. Its main goal is to provide security for devices with fewer resources. These limited‐resource devices implement reliable ciphers that use very little power and computation. The lightweight cipher should be built for high performance while using the fewest resources possible, such as memory and power. In this article, we compare block ciphers and several other stream ciphers based on criteria such as input size, output size, structure employed, key size, number of rounds, vulnerable attacks, chip area, gate equivalent, memory use, throughput, and security features. Moreover, this article provides a detailed analysis comparing all cryptographic algorithms and their use in day‐to‐day life activities. This paper also discusses some lightweight ciphers, stream ciphers, and hybrid ciphers. Moreover, it shows the cryptanalysis of some block ciphers like DES.

Towards the successful operation of any farm, effective livestock management is necessary. The efficiency, affordability, and scalability of management solutions play a crucial role in modern farming. In this paper, a novel low-cost framework is proposed to recognize for health monitoring of individual cattle based on their sensed behavioral activity data and muzzle (nose) pattern image database using incremental decision tree classification techniques and accelerometer-based activity monitoring method. The proposed system performs image prepossessing on the captured muzzle point images of cattle to mitigate the noise, enhance the contrast, and increase quality. We extract the minutiae features to improve the system’s accuracy for recognizing the cattle for accurate and early classification of behavioral activity for health monitoring. The proposed system uses a support vector machine and incremental decision tree classifiers to classify the extracted feature of cattle’s muzzle images and health database. The server has encrypted databases that consist of captured muzzle images, owner database, health sensor database provided by the owners and systems. We use a similarity score measurement using incremental decision tree classification for matching and classify the muzzle point featutes with the database for cattle identification and health monitoring. We also developed a prototype for evaluating the accuracy of the proposed system with 97.99% accuarcy for unique identification of individual cattle.

Chart images exhibit significant variabilities that make each image different from others even though they belong to the same class or categories. Classification of charts is a major challenge because each chart class has variations in features, structure, and noises. However, due to the lack of affiliation between the dissimilar features and the structure of the chart, it is a challenging task to model these variations for automatic chart recognition. In this article, we present a novel dissimilarity-based learning model for similar structured but diverse chart classification. Our approach jointly learns the features of both dissimilar and similar regions. The model is trained by an improved loss function, which is fused by a structural variation-aware dissimilarity index and incorporated with regularization parameters, making the model more prone toward dissimilar regions. The dissimilarity index enhances the discriminative power of the learned features not only from dissimilar regions but also from similar regions. Extensive comparative evaluations demonstrate that our approach significantly outperforms other benchmark methods, including both traditional and deep learning models, over publicly available datasets.

Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum‐rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance‐based face recognition algorithms has been done at each level.

A recognition system is very useful to recognize human, object, and animals. An animal recognition system plays an important role in livestock biometrics, that helps in recognition and verification of livestock in case of missed or swapped animals, false insurance claims, and reallocation of animals at slaughter houses. In this research, we propose a fast and cost-effective animal biometrics based cattle recognition system to quickly recognize and verify the false insurance claims of cattle using their primary muzzle point image pattern characteristics. To solve this major problem, users (owner, parentage, or other) have captured the images of cattle using their smart devices. The captured images are transferred to the server of the cattle recognition system using a wireless network or internet technology. The system performs pre-processing on the muzzle point image of cattle to remove and filter the noise, increases the quality, and enhance the contrast. The muzzle point features are extracted and supervised machine learning based multi-classifier pattern recognition techniques are applied for recognizing the cattle. The server has a database of cattle images which are provided by the owners. Finally, One-Shot-Similarity (OSS) matching and distance metric learning based techniques with ensemble of classifiers technique are used for matching the query muzzle image with the stored database.A prototype is also developed for evaluating the efficacy of the proposed system in term of recognition accuracy and end-to-end delay.