Journal of Intelligent Systems and Internet of Things

Internet of Things (IoT) devices are more attractive towards various vulnerable activities and nodes are easily compromised by attackers. The complexity of insecure IoT node installation relies on device heterogeneity and resource constraints because of the network ends and conventional endpoints. This work concentrates on modeling an efficient IoT-based preservation model () which is a lightweight approach used for detecting anomaly and performance various analyses at the endpoints. This work integrates linear Support Vector Machine for pattern analysis and adaptive fuzzy rule model for data pattern rule generation to examine malicious network functionality and network traffic. While adopting the rules, the compromised node needs to fulfill the generated rules; when it fails then it is considered as malicious activity. Then, the models impose network access restrictions on the compromised and terminate the further process. Thus, the nodes are prevented from further network attacks. The evaluation model is done with the use of an online available network dataset and the dataset samples are evaluated in the complex network scenario. The simulation is done in MATLAB 2020a simulation environment and the accuracy attained with this model is higher compared to other approaches. Similarly, other metrics like False Alarm Rate (FAR) are evaluated for predicting malicious network functionality. The significance of the model is evaluated based on the prediction and mitigation of various network attacks.  The anticipated model shows a prediction rate of 90.21% for DoS attacks, 89.13% for R2L, 91.65% for probe, and 93.56% for U2R attacks.

Forecasting energy demand is essential for efficient grid management as it promotes steady operations, efficient markets, and sustainable energy practices. In this study, previously observed, evenly spaced energy consumption data are analysed using recurrent neural networks based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to extract important insights, features, and remarkable patterns. First, the study examines the influence of meteorological features on energy consumption. The most significant meteorological features are determined by computing the MIC and Pearson's correlation coefficient. The selected features are then combined with historical energy consumption data to feed the neural network. Second, to improve and optimise the performance of the proposed models, two technical indicators - the daily energy usage average and the simple moving average - are considered. The following are some instances of comparisons in terms of prediction accuracy: (1) The MAPE of the proposed model is 2.47, whereas that of the current model is 4.03. (2) The MAPE of the existing model is 25.83, whereas the proposed solution is 18.68. (3) The MAPE of the suggested model is 24.8, while the MAPE of the current model is 26.6. (4) The MAPE of the present model is 4.77, whereas the suggested approach's is 4.42.

Multi Organ and tumor segmentation is the challenging task in medical imaging and surgical planning scenarios due to its diverse applications includes lesions and organs measurements and disease diagnosis respectively. Although collecting and examining labels for all classes pose severe challenges. Furthermore, Graphical Processing Unit (GPU) optimization emerge as another critical factor for multi organ and tumor segmentation. To address the mentioned conventional challenge, we designed a deep learning-based model named “Intelligent Segmentor” which performs automated segmentation in end-to-end fashion with novel semi supervised training approach. Initially, the obtained multi organ CT images is then subjected to pre-processing in terms of geometric standardization, noise removal, and intensity normalization respectively. The pre-processed image is then further provided to dual view training for effective Pseudolabel generation. The labelled data along with generated pseudolabels are provided to train the model for amplifying the model performance. After that, there are two inputs are provided to the designed segmentation model which includes dual encoders such as GoogleNet and VGG-16 for contextual and spatial information extraction in five stages, Tweaked Feature Pyramidal Network (TFPN) for dimensionality reduction and side features extraction, and Gated Fusion Module (GFM) for fusing the side features to form unified feature map. Finally, the unified feature map is the examined through convolution layers for multi organ and tumor output. We adopted FLARE 2023 dataset for validating the proposed work with existing works on 13 various organs and tumor segmentation tasks. From the results, the proposed research achieves better Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) through online validation and final testing than the existing works.

The recent progress in the Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing has revolutionized the traditional healthcare system, upgrading it into a smart healthcare system. Medical services can be enhanced by integrating essential technology such as IoT and AI. The integration of IoT and AI presents several prospects within the healthcare industry. In this research, a novel hybrid Deep Learning (DL) model called Binary Butterfly Optimization Algorithm with Stacked Non-symmetric Deep Auto-Encoder (BBOA-SNDAE) for HD (HD) prediction based on the Medical IoT technology. The key aim of the work is to categorize and predict HD utilizing clinical data with the BBOA-SDNAE model. Initially, the model is trained using the Cleveland and Statlog datasets. The input data is preprocessed and standardized utilizing the Min-Max normalization. After preprocessing, the selection of features is performed utilizing the BBOA to choose the best optimal features for improved classification. Based on the selected features, the classification is performed using the SNDAE technique. The research model was assessed based on accuracy, sensitivity, precision, specificity, NPV, and F-measure. The model attained 99.62% accuracy, 99.45% precision, 99.32% NPV, 99.56% sensitivity, 99.45% specificity, and 99.38% f-measure using the HD dataset, and the model attained 98.84% accuracy, 98.73% precision, 98.34% NPV, 98.62% sensitivity, 98.21% specificity, and 98.27% f-measure using the sensor data. The results of the research model were compared with the current model for validation, where the research model outperformed all the compared models.

Research on wireless body area networks (WBAN), also known as wireless body sensor networks (WBSN), has been increasingly important in medical applications recently and is now crucial for patient monitoring. To create a dependable body area network (BAN) system, several factors need to be considered at both the software and hardware levels. One such factor is the designing and implementation of routing protocols in the network layers. Protocols for routing can detect and manage the routing paths in a network to facilitate efficient data transmission between nodes. Therefore, the routing protocol is crucial in wireless sensor networks (WSN) to provide dependable communication among the sensor nodes. Different clustering methods can be used in WBAN systems. However, these techniques often produce many cluster heads (CHs), which leads to higher energy consumption. Increased consumption of energy reduces the lifespan of WBANs and raises costs of monitoring. This research proposes a recent metaheuristic algorithm to select the optimal clusters to provide an energy-effective protocol for healthcare monitoring. This research aims to minimize the energy utilization of WBANs by choosing the most suitable CHs based on the BWO. The proposed BWO-based routing protocol demonstrates superior performance in WBANs based on energy consumption, packet loss, packet delivery ratio, network lifetime, end-to-end delay, and throughput. It optimizes energy consumption by effectively selecting CHs and routing paths, leading to balanced energy usage and prolonged network operation. The BWO model significantly reduces end-to-end delay by ensuring data packets follow the shortest and least congested routes, which is significant for real-time health monitoring. It achieves a high packet delivery ratio, typically between 95% and 98%, indicating reliable data transmission, while maintaining a low packet loss rate, generally between 1% and 5%. Additionally, the BWO-based routing protocol extends network lifetime by preventing early node depletion and enhances network throughput by reducing congestion and packet collisions, thereby supporting continuous and robust health data monitoring.

One example of a cutting-edge technology that is opening up new channels for human-machine connection is brain-computer interfaces, or BCIs. From keyboards and mice to touchscreens, voice commands, and gesture engagements, communication interfaces have evolved over time. New methods of controlling computer systems and engaging with virtual worlds have gained appeal as computers become more and more ingrained in daily life. These innovative applications range from gaming to teaching. It's important to handle ethical, privacy, and security issues related to developing and applying Brain-Computer Interface (BCI) technology from a balanced standpoint. Susceptible brain signals must be gathered and interpreted for BCI devices. Unauthorized access to this material carries the risk of compromising privacy by disclosing private thoughts, feelings, or other sensitive information. The initial areas of brain-computer interface (BCI) applications were based on EEG and created for medical use, hoping to help patients get back to their regular lives. Beyond the original purpose, EEG-based BCI applications have become more and more important in the non-medical field, helping healthy individuals live better lives by becoming more productive, collaborative, and self-developing, for example.

Clothing design plays an important role in personal image expression and social and cultural transmission. The traditional fashion design method has many problems, such as low efficiency and large design error, and it is difficult to bring users better wearing experience. In order to meet different users’ Design needs, reduce design errors, and improve users’ satisfaction with design results, this paper combined with intelligent sensing technology, conducted in-depth research on digital automation analysis of clothing design CAD (Computer Aided Design). Aiming at the clothing design process, this paper first constructed a brand-new clothing design CAD system, using the depth transducer to solve the 3D information of the relevant feature points, and realized the accurate acquisition of the human body feature size information. Through the registration of adjacent frame point data, the 3D human body modeling was carried out. Then, according to the user’s physical characteristics and related information collected by the sensor, the paper compared the user’s characteristic information to filter out the user’s preferences, and used the recommendation algorithm to calculate the corresponding parameters to realize the intelligent choice of clothing styles. Finally, through the measurement of each index by the sensor, the size adjustment of the garment and the specific design of the garment were realized. In order to verify the effect of clothing design CAD system based on intelligent sensing technology, this paper conducted system tests. The results showed that in terms of clothing comfort, clothing quality and clothing functionality, the number of users satisfied and very satisfied reached 50.4%, 47.9% and 51.3%, respectively. From the overall survey results, the system has a high degree of user satisfaction. The research conclusion of this paper shows that the digital automatic analysis of clothing design CAD based on intelligent sensing technology can effectively meet the needs of users, improve their wearing experience, and promote the intelligent development of clothing design.

In the evolving landscape of the Internet of Things (IoT), effective intrusion detection is paramount for maintaining security and data integrity. This study introduces a hybrid heuristic technique utilizing artificial intelligence for enhancing intrusion detection systems (IDS) in IoT environments. By integrating various machine learning models, the research focuses on training, tuning, and validating a sequential neural network to predict intrusion occurrences based on extensive data analysis. The methodology involves modelling, which starts with training machine learning algorithms to predict labels from features, tuning the models to meet organizational requirements, and validating them using holdout data. Key machine learning techniques explored include logistic regression, k-nearest neighbors (KNN), naive Bayes, support vector machines (SVM), decision trees, random forests, and neural networks. Each technique's applicability to classification tasks, particularly binary and multivariate scenarios, is discussed in the context of enhancing IDS capabilities. A sequential neural network model, comprising multiple dense and dropout layers, was developed and trained with 148,033 parameters to achieve high accuracy and robustness. The architecture's effectiveness in learning intricate patterns associated with malicious activities while avoiding overfitting is emphasized. The study demonstrates the model's proficiency in binary classification tasks, which is critical for distinguishing between normal and anomalous behaviors in IoT systems. The results indicate that the neural network, optimized using the hybrid heuristic approach, shows a significant reduction in validation loss and a steady improvement in accuracy over multiple epochs. Despite initial overfitting signs, the model maintains high performance on unseen data, underscoring the importance of ongoing model assessment and tuning.