Virtual Reality (VR) provides immersive and interactive experiences in healthcare, education, entertainment, and defense. However, cybersickness remains a major barrier to its widespread adoption, reducing user comfort and engagement. Early and accurate detection of cybersickness is critical to developing adaptive VR systems that ensure safety and improve usability. In this study, we propose a novel real-time cybersickness detection approach using Bidirectional Long Short-Term Memory (Bi-LSTM) networks trained on electroencephalography (EEG) signals. Power Spectral Density (PSD) and Signal Magnitude Area (SMA) features were extracted to capture frequency- and amplitude-related characteristics of cybersickness. EEG data were collected from six electrodes across frontal (F3–F4), prefrontal (FP1–FP2), and central parietal (P3–P4) regions during VR exposure. The proposed Bi-LSTM model achieved 95% classification accuracy, significantly outperforming baseline methods. Results indicate that cybersickness can be reliably detected with a compact EEG setup, supporting resource-efficient, real-time monitoring for adaptive VR environments.

Strokes still stand as one of the most common, as well as fatal, causes of disability. Prompt, accurate diagnosis is essential for recovery, and effective treatment depends on it. With radiologists being the prime career involved in CT or MRI interpretation, this may take much time and be prone to inaccuracy. Deep learning mainly promotes the concept of a total automation boost to the diagnostic capabilities of medical imaging, proving to be a quick answer. The Stroke Detective framework, which utilizes deep learning algorithms, classifies whether a stroke on a CT scan is ischemic versus hemorrhagic, as well as the specific location of the damage within the brain that resulted in hemiplegia. In the pursuit of achieving pixel-wise segmentation of brain images (CT and MRI), the Deep Lab version 3 (DeepLabV3) continues its performance on precise segmentation of stroke-invoked areas. This segmentation, by measuring and analyzing the affected areas, is such a tool for stroke diagnosis, which can directly impact patient outcomes and therapeutic approaches. DeepLabV3: This encoder-decoder architecture, which employs ASPP and is built upon a deep convolutional neural network (CNN), is thought to be a very promising way to enhance stroke identification and segmentation in brain scans. To accurately segment stroke areas in complex brain images, DeepLabV3 recommends an encoder-decoder architecture structure that enables the model to identify details at the edges of lesions. The DeepLabV3+ with ASPP Modules permits the model to pay attention to both large and minute brain lesions. The ASPP introduced by the model enables successful retrieval of contextual information about multi-scales by using atrous (or dilated) convolutions with increasing rates. The ASPP captures image features at multiple scales, hence helping us segment those lesions that are challenging to identify owing to subtle differences in their size and texture. Various experiments have shown that the DeepLabV3 model outperforms the current best methods. The model demonstrates superior potential for lesion localization and boundary accuracy on stroke MRI and CT imaging datasets in comparison with patchy, dreary, repetitive models, thus assisting lesion segmentation. Advocates the integration of the DeepLabV3+ and ASPP for stroke detection and segmentation and, hence, will provide a functional tool to enhance diagnostic precision and facilitate timely intervention decisions by a physician.

Abstract Dentistry frequently makes use of intraoral scanning technologies to digitally acquire the three‐dimensional (3D) geometry of teeth. In recent times, dental clinics over the globe utilize used computer aided diagnosis (CAD) models to make treatment plans, for example, orthodontics. Orthodontic CAD system acts as a vital part of the advanced dentistry field. A 3D dental model, computed by patient impression, as input and aids dentist in the extraction, moving, deletion, and rearranging of teeth to simulate treatment output. Tooth segmentation and labelling is the basic and foremost element of the CAD model which needs to be addressed. Automated segmentation and classification of 3D dental images using advanced machine learning and deep learning (DL) models become essential. This article introduces a new 3D dental image segmentation and classification using DL with tunicate swarm algorithm (3DDISC‐DLTSA) model. The major intention of the 3DDISC‐DLTSA system is to segment the tooth model and identify seven distinct tooth types. To accomplish this, the presented 3DDISC‐DLTSA model performs image pre‐processing in two stages namely image filtering and U‐Net segmentation. In addition, the 3DDISC‐DLTSA model derives DenseNet‐169 model for feature extraction purposes. For the recognition and classification of tooth type, the TSA based hyperparameter tuning process is carried out which helps to accomplish maximum classification performance. A wide range of experimental analyses is performed and the outcomes are inspected under many aspects. On dataset‐1, 3DDISC‐DLTSA model accuracy rose by 96.67%. On dataset‐3, 3DDISC‐DLTSA model accuracy rose by 97.48% and algorithm accuracy by 97.35%. The 3DDISC‐DLTSA model outperformed more modern models, according to the comparative investigation.

Machine learning (ML) is currently a crucial tool in the field of cyber security. Through the identification of patterns, the mapping of cybercrime in real time, and the execution of in-depth penetration tests, ML is able to counter cyber threats and strengthen security infrastructure. Security in any organization depends on monitoring and analyzing user actions and behaviors. Due to the fact that it frequently avoids security precautions and does not trigger any alerts or flags, it is much more challenging to detect than traditional malicious network activity. ML is an important and rapidly developing anomaly detection field in order to protect user security and privacy, a wide range of applications, including various social media platforms, have incorporated cutting-edge techniques to detect anomalies. A social network is a platform where various social groups can interact, express themselves, and share pertinent content. By spreading propaganda, unwelcome messages, false information, fake news, and rumours, as well as by posting harmful links, this social network also encourages deviant behavior. In this research, we introduce Deep Belief Network (DBN) with Triple DES, a hybrid approach to anomaly detection in unbalanced classification. The results show that the DBN-TDES model can typically detect anomalous user behaviors that other models in anomaly detection cannot.

Electronic Health Record (EHR) systems are a valuable and effective tool for exchanging medical information about patients between hospitals and other significant healthcare sector stakeholders in order to improve patient diagnosis and treatment around the world. Nevertheless, the majority of the hospital infrastructures that are now in place lack the proper security, trusted access control, and management of privacy and confidentiality concerns that the current EHR systems are supposed to provide. Goal. For various EHR systems, this research proposes a Blockchain-enabled Hyperledger Fabric Architecture as a solution to this delicate issue. The three steps of the suggested system are the secure upload phase, the secure download phase, and authentication. Patient registration, login, and verification make up the authentication step. The administrator grants authorization to read, edit, delete, or revoke the files following user details verification. In the secure upload phase, feature extraction is carried out first, and then a hashed access policy is created from the extracted feature. Next, the hash value is stored in an IoT-based Hyperledger blockchain. The uploaded EHR files are additionally encrypted before being stored on the cloud server. In the secure download step, the physician uses a hashed access policy to send the request to the cloud and decrypts the corresponding files. The experimental findings demonstrate that the system outperformed cutting-edge techniques. The proposed Modified Key Policy Attribute-Based Encryption performs better for the remaining 10 to 25 mb file sizes. This IoT framework compares MKP-ABE with certain efficiency indicators, such as encryption, decryption period, protection level analysis and encrypted memory use, resource use on decryption, upload time, and transfer time, which are present in the KP-ABE, the ECC, RSA, and AES. Here, the IoT device suggested requires 4008 ms for data encryption and 4138 ms for the data decryption.

Summary Due to the exponential increase in the generation of digital documents and in the online search user diversity, multilingual information is highly available on the Internet. However, the huge amount of multilingual data cannot be analyzed manually. Therefore, cross lingual multi‐document summarization (CLMDS) model is introduced to generate a summary of several documents in which the summary language is different from the source document language. This paper presents a Deep Learning Enabled Cross‐lingual Search with Metaheuristic based Query Optimization (DLCLS‐MQO) model for Multi‐document summarization. The DLCLS‐MQO model allows to offer a query in Tamil, summarize several English documents, and lastly translate the summary into Tamil. The DLCLS‐MQO model encompasses four stages of operation such as multilingual search, query optimization, automatic sematic lexicon builder, and document summarization. Firstly, bidirectional long short‐term memory (BiLSTM) model is applied to perform multilingual searching process. Followed by, sunflower optimization (SFO) algorithm based query optimization process is carried out. Moreover, global vectors (GloVe) method is used for the construction of domain oriented sentiment lexicons. Finally, extreme gradient boosting (XGBoost) model is applied for the CLMDS. A detailed simulation analysis takes place to highlight the betterment of the DLCLS‐MQO model. The resultant experimental values portrayed the superior performance of the DLCLS‐MQO model over the compared methods.

Forests are essential natural resources that directly impact the ecosystem. However, the rising frequency of forest fires due to natural and artificial climate change has become a critical issue. A revolutionary municipal application proposes deploying an artificial intelligence‐based forest fire warning system to prevent major disasters. This work aims to present an overview of vision‐based methods for detecting and categorizing forest fires. The study employs a forest fire detection dataset to address the classification difficulty of discriminating between photos with and without fire. This method is based on convolutional neural network transfer learning with Inception‐v3. Thus, automatic identification of current forest fires (including burning biomass) is a critical field of research for reducing negative repercussions. Early fire detection can also assist decision‐makers in developing mitigation and extinguishment strategies. Radial basis function Networks (RBFNs) with rapid and accurate image super resolution (RAISR) is a deep learning framework trained on an input dataset to detect active fires and burning biomass. The proposed RBFN‐RAISR model’s performance in recognizing fires and nonfires was compared to earlier CNN models using several performance criteria. The water wave optimization technique is used for image feature selection, noise and blurring reduction, image improvement and restoration, and image enhancement and restoration. When classifying fire and no‐fire photos, the proposed RBFN‐RAISR fire detection approach achieves 97.55% accuracy, 93.33% F‐Score, 96.44% recall, 94.19% precision, and an error rate of 24.89. Given the one‐of‐a‐kind forest fire detection dataset, the suggested method achieves promising results for the forest fire categorization problem.

Fingerprint recognition has emerged as one of the most reliable biometric authentication methods , owing to its uniqueness and permanence. However, the security and confidentiality of the user’s data are key considerations in modern biometric systems. In this study, we describe an intelligent computational technique for automatically validating fingerprints for identification and verification purposes. The feature vector is created by fusing Gabor filtering features with deep learning techniques like the faster region-based convolutional neural network (Faster R-CNN). This study uses linear and decision-based median filtering (DBMF) techniques to minimize visual impulse noise. Faster-R-CNN with DBMF was applied to the feature vectors to reduce overfitting problems while improving classification precision and reliability. For fingerprint matching, the Euclidean distance between the associated Harris-SURF feature vectors of two feature points is used to measure feature-matching similarity between two fingerprint images . Furthermore, for fine-tuned matching an iterative technique known as RANSAC (Random Sample Consensus) is used. The experimental results collected from the public-domain fingerprint databases FVC-2002 DB1 and FVC-2000 DB1 show that the proposed design is viable and performs well with an accuracy of 99.43%, MSE value of 43.321%, and an execution time of 3.102 ms which was more exact than existing models.

Summary Because of recent breakthroughs in information technology, the Internet of Things (IoT) is becoming increasingly popular in a variety of application areas. Wireless sensor networks (WSN) are a critical component of IoT systems, and they consist of a collection of affordable and compact sensors that are utilized for data collecting. WSNs are used in a variety of IoT applications, such as surveillance, detection, and tracking systems, to sense the surroundings and transmit the information to the user's device. Smart gadgets, on the other hand, are limited in terms of resources, such as electricity, bandwidth, memory, and computation. A fundamental issue in the IoT‐based WSN is to achieve energy efficiency while also extending the network's lifetime, which is one of the limits that must be overcome. As a result, energy‐efficient clustering and routing algorithms are frequently employed in the IoT system. As a result of this inspiration, the authors of this research describe an Energy Aware Clustering and Multihop Routing Protocol with mobile sink (EACMRP‐MS) technique for IoT supported WSN. The EACMRP‐MS technique's purpose is to efficiently reduce the energy consumption of IoT sensor nodes, consequently increasing the network efficiency of the IoT system. The suggested EACMRP‐MS technique initially relies on the Tunicate Swarm Algorithm (TSA) for cluster head (CH) selection and cluster assembly, as well as the TSA. Furthermore, the type‐II fuzzy logic (T2FL) technique is used for the optimal selection of multi‐hop routes, with multiple input parameters being used to achieve this. Finally, a mobile sink with route adjustment scheme is presented to further increase the energy efficiency of the IoT system. This scheme allows for the adjustment of routes based on the trajectory of the mobile sink, which further improves the energy efficiency of the system. Using a detailed experimental analysis and simulation findings, it was discovered that the EACMRP‐MS technique outperformed the most recent state of the art methods in terms of a variety of evaluation metrics, indicating that it is a promising alternative.

Rapid advancements of sixth-generation (6G) network and Internet of Everything (IoE) supports numerous emerging services and application. Increasing mobile internet traffic and services, on the other hand, presented a number of challenges that could not be addressed with the current network design. The cybertwin is equipped with a variety of capabilities, including communication assistants, network data loggers, and digital asset owners, to address these difficulties. While spectrum resources are limited, effective resource management and sharing are essential in achieving these requirements. With this motivation, this article presents a new metaheuristic with blockchain based resource allocation technique (MWBA-RAT) for cybertwin driven 6G on IoE environment. The incorporation of the blockchain in 6G enables the network to monitor, manage, and share resources effectively. The proposed MWBA-RAT technique designs a new quasi-oppositional search and rescue optimization (QO-SRO) algorithm for the optimal resource allocation process and this shows the novelty of the work. The QO-SRO algorithm involves the integration of the quasi oppositional based learning concept with the traditional SRO algorithm to improve its convergence rate. A wide range of experiments are performed to highlight the enhanced outcomes of the MWBA-RAT technique.