Underwater Wireless Sensor Networks (UWSNs) have emerged as a remarkable interest for scholars worldwide in terms of various applications such as monitoring offshore oil and gas reservoirs, pollution, oceans for defense, and other applications such as tsunami. Terrestrial Wireless Sensor Networks (TWSN) and UWSNs share many characteristics apart from having different communication medium and working environment as UWSNs face the challenges of low-bandwidth, long latency, and high bit error rate. These have caused for UWSNs many problems such as low reliability, packet retransmission, and high consumption of energy. To alleviate the aforementioned issues, many techniques have been proposed. However, most of them merely consider the issue of hotspot which occurs due to the unbalanced transmission of load on sensor nodes near the surface sink. In this article, we propose a multi-layer cluster-based Energy Efficient (MLCEE) protocol for UWSNs to address the issue of hotspot and energy consumption. There are different stages in MLCEE, first of which is the division of the whole network in layers, the second is clustering of the nodes at same layers. In the last stage of transmission, the cluster head (CH) selects the next hop among the CHs based on greater fitness value, small Hopid and small layer number. To mitigate the issue of hotspot, the first layer remains un-clustered and any node in the first layer transfers data to the sink directly while cluster heads (CHs) are selected based on Bayesian Probability and residual energy. The simulation results of the proposed technique, done using MATLAB, have revealed that MLCEE achieves superior performance than the other techniques with regard to the network lifetime, energy consumption, and data transmission amount.

360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user’s familiarity with VR and user’s interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.

Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.

360-degree Virtual Reality (VR) videos have already taken up viewers’ attention by storm. Despite the immense attractiveness and hype, VR conveys a loathsome side effect called “cybersickness” that often creates significant discomfort to the viewers. It is of great importance to evaluate the factors that induce cybersickness symptoms and its deterioration on the end user’s Quality-of-Experience (QoE) when visualizing 360-degree videos in VR. This manuscript’s intent is to subjectively investigate factors of high priority that affect a user’s QoE in terms of perceptual quality, presence, and cybersickness. The content type (fast, medium, and slow), the effect of camera motion (fixed, horizontal, and vertical), and the number of moving targets (none, single, and multiple) in a video can be the factors that may affect the QoE. The significant effect of such factors on end-user QoE under various stalling events (none, single, and multiple) is evaluated in a subjective experiment. The results from subjective experiments show a notable impact of these factors on end-user QoE. Finally, to label the viewing safety concern in VR, we propose a neural network-based QoE prediction method that can predict the degree of cybersickness influenced by 360-degree videos under various stalling events in VR. The performance accuracy of the proposed method is then compared against well-known Machine Learning (ML) algorithms and existing QoE prediction models. The proposed method achieved a 90% prediction accuracy rate and performed well against existing models and other ML methods.

Brain tumors provide a significant healthcare concern worldwide due to their potentially lethal consequences and the intricate nature of their diagnosis. These tumors exhibit significant variability in kind, size, and location, hence confounding identification and treatment efforts. Timely and precise detection is essential, as it profoundly influences treatment efficacy and survival probabilities. Contemporary diagnostic techniques, predominantly reliant on Magnetic Resonance Imaging (MRI), necessitate considerable manual analysis by experts, resulting in possible delays and inconsistencies in diagnosis. In response to the urgency of these difficulties, our research presents a novel multi-task learning methodology utilizing advanced neural network architectures to automate and improve the accuracy of brain tumor identification and classification from MRIs. This method seeks to optimize the diagnostic procedure, diminish reliance on manual analysis, and deliver swift, dependable outcomes that can expedite the commencement of treatment. The efficacy of our methodology is evidenced by comprehensive testing of three sophisticated neural architectures: UNet, Attention-UNet, and Residual-Attention-UNet. Our results indicate that the Residual-Attention-UNet model significantly surpasses the others in segmentation accuracy and classification precision. Our studies, utilizing conventional metrics including the Jaccard Similarity Index (JSI), Dice Coefficients (DC), and overall Accuracy (ACC), demonstrated that the Residual-Attention-UNet attained roughly 89.30% JSI, 91.10% DC, and 93.35% ACC. In binary classification tasks, this model achieved a Precision of 98.60%, Recall of 98.06%, Accuracy of 99.40%, and an F1 score of 96.57%. Furthermore, in multiclass classification contexts, the model consistently above 95% across all measures, underscoring its robustness and the efficacy of our proposed multi-task learning approach. These results highlight the capability of our method to substantially progress the domain of medical imaging for brain tumors, providing a robust instrument for improving diagnostic precision and patient management in neuro-oncology.