False Prediction Rate Research Articles

PHASE TRANSITION-BASED OPTIMIZED ADAPTING MODEL FOR REAL-TIME SEIZURE PREDICTION APPLICATION

Epilepsy is a dynamic process that will undoubtedly continue throughout an individual’s lifetime. One part of the brain may get affected, other brain regions also can possibly get involved in this disorder. Such seizures are sometimes life threatening; therefore, a warning signal is required to provide information to their respective caretakers in order to ensure the safety of the epileptic patients. The following are the four states of seizure: interictal (between seizures), preictal (before seizure), ictal (seizure), and postictal (after seizures). The ability to accurately distinguish the unique preictal state from the other seizure states is essential for seizure prediction. The Seizures can only be predicted by early detection of the preictal condition, which helps to differentiate seizure prediction from seizure detection. Preictal signals, which typically appear minutes to hours before a seizure occurs, are predicted in order to prevent seizures. In this paper, the discussions on the seizure predictions and its procedures are done. An optimized adapting model based on the phase transitions for real-time seizure prediction is developed and implemented in this study. We proposed a classification model called as enhanced convolutional neural networks (ECNNs) that has been tuned for speeding convergence and reducing model complexity utilizing the Fletcher Reeves Algorithm (WO-FRA) based on Walrus Optimization. Furthermore, the Phase Transition Predictor (PTP) based on the Kullback–Leibler (KL) divergence yields a Premium Seizure Prediction Horizon (PSPH). The suggested model’s empirical results, which were verified using 1000 EEG recordings from the CHB-MIT, NINC, and SRM databases, outperform the current techniques with 98% accuracy, 0.07 h[Formula: see text] false prediction rate and with 99% sensitivity.

Combination of Channel Reordering Strategy and Dual CNN-LSTM for Epileptic Seizure Prediction Using Three iEEG Datasets.

Intracranial electroencephalogram (iEEG) signals are generally recorded using multiple channels, and channel selection is therefore a significant means in studying iEEG-based seizure prediction. For n channels, [Formula: see text] channel cases can be generated for selection. However, by this means, an increase in n can cause an exponential increase in computational consumption, which may result in a failure of channel selection when n is too large. Hence, it is necessary to explore reasonable channel selection strategies under the premise of controlling computational consumption and ensuring high classification accuracy. Given this, we propose a novel method of channel reordering strategy combined with dual CNN-LSTM for effectively predicting seizures. First, for each patient with n channels, interictal and preictal iEEG samples from each single channel are input into the CNN-LSTM model for classification. Then, the F1-score of each single channel is calculated, and the channels are reordered in descending order according to the size of F1-scores (channel reordering strategy). Next, iEEG signals with an increasing number of channels are successively fed into the CNN-LSTM model for classification again. Finally, according to the classification results from n channel cases, the channel case with the highest classification rate is selected. Our method is evaluated on the three iEEG datasets: the Freiburg, the SWEC-ETHZ and the American Epilepsy Society Seizure Prediction Challenge (AES-SPC). At the event-based level, the sensitivities of 100%, 100% and 90.5%, and the false prediction rates (FPRs) of 0.10/h, 0/h and 0.47/h, are achieved for the three datasets, respectively. Moreover, compared to an unspecific random predictor, our method also shows a better performance for all patients and dogs from the three datasets. At the segment-based level, the sensitivities-specificities-accuracies-AUCs of 88.1%-94.0%-93.5%-0.9101, 99.1%-99.7%-99.6%-0.9935, and 69.2%-79.9%-78.2%-0.7373, are attained for the three datasets, respectively. Our method can effectively predict seizures and address the challenge of an excessive number of channels during channel selection.

Anchoring temporal convolutional networks for epileptic seizure prediction.

Objective. Accurate and timely prediction of epileptic seizures is crucial for empowering patients to mitigate their impact or prevent them altogether. Current studies predominantly focus on short-term seizure predictions, which causes the prediction time to be shorter than the onset of antiepileptic, thus failing to prevent seizures. However, longer epilepsy prediction faces the problem that as the preictal period lengthens, it increasingly resembles the interictal period, complicating differentiation.Approach. To address these issues, we employ the sample entropy method for feature extraction from electroencephalography (EEG) signals. Subsequently, we introduce the anchoring temporal convolutional networks (ATCN) model for longer-term, patient-specific epilepsy prediction. ATCN utilizes dilated causal convolutional networks to learn time-dependent features from previous data, capturing temporal causal correlations within and between samples. Additionally, the model also incorporates anchoring data to enhance the performance of epilepsy prediction further. Finally, we proposed a multilayer sliding window prediction algorithm for seizure alarms.Main results. Evaluation on the Freiburg intracranial EEG dataset shows our approach achieves 100% sensitivity, a false prediction rate (FPR) of 0.09 per hour, and an average prediction time (APT) of 98.92 min. Using the CHB-MIT scalp EEG dataset, we achieve 97.44% sensitivity, a FPR of 0.12 per hour, and an APT of 93.54 min.Significance. These results demonstrate that our approach is adequate for seizure prediction over a more extended prediction range on intracranial and scalp EEG datasets. The APT of our approach exceeds the typical onset time of antiepileptic. This approach is particularly beneficial for patients who need to take medication at regular intervals, as they may only need to take their medication when our method issues an alarm. This capability has the potential to prevent seizures, which will greatly improve patients' quality of life.

Environment Aspects and Daily Life-Threatening Risk Prediction for Improving Public Health Using Ensemble Learning Techniques

Increasing work nature in the fastest world need relaxing to maintain public health is important. But the continuous imbalance of work nature in human daily life doesn’t have time to consecrate the health with leads life threatening facts such as work tension, management stress, job professional, family needs, improper periodic work cycle, and soon. By analyzing the daily public life threatening risk prediction based on Feature selection and classification to category the public health to recommend for phycological treatment. By suggesting treatment schedule are important to balance the public health Lifecyle to make stress free life to spent with nature. Most of prevailing techniques analyse the life-threatening issues, but the features are improperly to taken without the mutual relation cause poor accuracy in precision and classifications rate with more false prediction rate. To tackle this issue, to propose an optimized ensemble learning Techniques based on multi scalar support vector machine (MSSVM) with deep scaled XGboost classifier (DSXG-boost) to improve the prediction accuracy. Initially the public life Cycle dataset is collected and to make normalization using Min-max normalization. Then the Public Life Threat Impact Rate (PLTIR) is analysed to marginalize the heath affecting features. Then then stress margin dependencies feature limits are selected with support of MSSVM. The selected features are grouped in cluster margins and classified with deep scaled XG boost algorithm to predict the active and inactive heath margin be categorized by risk threaten class. The proposed system prove the prediction accuracy in higher precision are by selecting mutual dependencies of health affection feature limits and best recall rate to improve the performance. Based on the predicted class, the categorized life threaten risk peoples are recommends to make phycological treatment to protect the public life cycle.

Optimizing Startup Success Prediction Through SMOTE Oversampling and Classification

Rapid technological advancements have led to a surge in the number of startups competing with innovative ideas. Predicting the chances of a startup's future success becomes crucial for entrepreneurs in making informed decisions and strategizing their growth. This study investigates the effectiveness of the Gradient Boosting classification algorithm in predicting startup success. To address potential class imbalance within the dataset, a pre-processing step utilizing Synthetic Minority Oversampling Technique (SMOTE) was employed. The dataset itself encompassed a wide range of variables related to startup attributes and performance metrics. The F1-score metric was utilized to evaluate the model's accuracy while minimizing false positive predictions that could potentially mislead investors. Gradient Boosting algorithm was employed to analyze the dataset, which was pre-processed using SMOTE to handle potential class imbalance. This technique helps to create synthetic data points for the minority class, resulting in a more balanced dataset for the classification model. The dataset itself encompassed a wide range of variables related to startup attributes and performance. The F1-score metric was utilized to evaluate the model's accuracy while minimizing false positive predictions that could potentially mislead investors. Gradient Boosting algorithm achieved an F1-score of 86% for predicting successful startups and 85% for predicting unsuccessful ones. The low false positive prediction rate of 7.9% on the test data further validates the model's reliability. The findings demonstrate the effectiveness of Gradient Boosting in predicting startup success with high accuracy and minimal false positives

Short-term epileptic seizures prediction based on cepstrum analysis and signal morphology

This article aims to provide and implement a patient-specific seizure (for Intervention Time (IT) detection) prediction algorithm using non-invasive data to develop warning devices to prevent further patient injury and reduce stress. Employing algorithms with high initial data volume and computations time to increase the accuracy is an important problem in prediction issues. Consequently, reduction of calculations is met by applying only two effective EEG signal channels without manual removal of artifacts by visual inspection as the algorithm’s input. Autoregression (AR) modeling and Cepstrum detect changes due to IT period. We carry out the goal of higher accuracy by increasing sensitivity to interictal epileptiform discharges or artifacts and reduce errors caused by them, taking advantage of the discrete wavelet transform and the comparison of two channels epochs by applying the median filter. Averaging and positive envelope methods are introduced to patient-specific thresholds become more differentiated as soon as possible and can be lead to sooner prediction. We examined this method on a mathematical model of adult epilepsy as well as on 10 patients with EEG data. The results of our experiments confirm that performance of the proposed approach in accuracy and average false prediction rate is superior to other algorithms. Simulation results have been shown the robustness of our proposed method to artifacts and errors, which is a step towards the development of real-time alarm devices by non-invasive techniques.

A Consistent Augmented Stacking Polynomial Optimized Tool (ASPOT) for Improving Security of Cloud-IoT Systems

The cloud computing transforms information technology by offering end users simulated, flexible resources on demand that require fewer resources and facilities giving them greater flexibility. These materials are delivered over the Internet using predefined networking protocols, regulations, and styles, and they are overseen by various management groups. There are flaws and vulnerabilities in the underlying technology and legacy protocols that could allow an attacker to get access. A recent assessment of the literature led to the conclusion that most intelligence algorithms have a number of complex issues, including a high false prediction rate, difficulty classifying threats, high processing costs, and system load. Hence, the proposed work aims to develop an innovative and lightweight Augmented Stacking Polynomial Optimized Tool (ASPOT) for strengthening the cloud-IoT system security against modern cyberattacks with a accuracy of 99%. The current study uses the lightweight Deep Augmented Preprocessing Model (DAPM) to clean and normalize the input cyber-attack dataset by executing transformation and normalization operations on it. Furthermore, the Binary Sand Cat Swarm Optimization (BSCSO) technique is utilized to identify the most significant and relevant features of the normalized dataset in an optimal manner. Moreover, the class of assaults is promptly and precisely identified from the given data by applying the Deep Stacking Polynomial Learning Network (DSPLN) technology. The effectiveness and results of the proposed ASPOT method are analysed with the use of current cyber-attack statistics and a variety of assessment metrics.

Adversarial Network Model Based on Feature Fusion Learner for Intrusion Detection in Sensor Networks

The adversarial machine learning approaches are modelled to provide a defence mechanism during the prediction of cloning and jamming attacks launched over the wireless communication process. The transmitter is supplied with a pre-trained classifier to analyze the status of the channel based on the sensing nature and determine the other transmission process. The learning method gathers all acknowledgements and fusion made between nodes and the channel's current state to build a learning model that can accurately identify the succeeding transmission constraint caused by network jamming. In this instance, compared to random jamming procedures, an inventive anti-clone detection strategy aims to minimize the number of clones and jamming found throughout the network model. The transmitter analyzes the power restrictions over the sensor networks using the learning-based fisher score (FS). Here, an adversarial network model (ANM-FS) is fused to diminish the computational time to collect the training dataset by examining the incoming samples. With this defence mechanism, the transmitter intends to predict the false prediction rate (FPR) and design a better model for providing a reliable classifier. Systematically, the transmitter identifies the floating of attacks over the network model and adopts the defending mechanism to mislead the injected clone, enhancing the throughput and reducing the prediction error.

Predicting Functional Outcome After Ischemic Stroke Using Logistic Regression and Machine Learning Models

This research employed binary logistic regression and machine learning techniques; Decision Tree, Random Forest, and Support Vector Machine (SVM), to predict functional outcomes following ischemic stroke. The main goal was to determine the most suitable model for the dataset through a comprehensive performance evaluation. Four models were examined for predicting post-ischemic stroke functional outcomes: Decision Tree, Random Forest, Logistic Regression, and SVM. The evaluation involved metrics such as Accuracy, Precision, F1-Score, and Recall. The Logistic Regression model achieved the highest accuracy at 90%, accurately predicting outcomes in 90% of cases. However, it had lower precision (50%), indicating an increased rate of false positive predictions. On the other hand, the SVM model displayed the highest precision (71.3%), implying fewer false positive predictions. It also attained the highest F1-Score (77.5%), indicating a strong balance between precision and Recall compared to the other models. Notably, the Logistic Regression model achieved perfect Recall (100%), correctly identifying all positive outcomes, while the Random Forest model showed significant recall performance (93.2%). Conversely, the Decision Tree model exhibited moderate accuracy (66.11%) but lower precision (66%), F1-Score (6.15%), and recall (3.2%), suggesting challenges with false positives and false negatives. Choosing the best model depends on analysis priorities. For accurate identification of positive outcomes, the Logistic Regression model's perfect recall is advantageous. For balanced performance, the SVM model's high F1-Score makes it a compelling option.

Patient-specific warning of epileptic seizure upon shapelets features

Epilepsy is an intractable chronic neurological disease attached to extensive attention. Due to the fact that unpredictable seizure attacks result in serious physical injuries, early warning before seizure occurrence can help patients to get timely treatment and intervention. This paper presents a novel patient-specific method to predict epileptic seizures by learning shapelets of scalp electroencephalogram (EEG) signals recorded from different channels. In the proposed method, EEG signals are preprocessed to raise the Signal to Noise Rate (SNR). Multichannel shapelets space is constructed by the learning-near-to-optimal shapelets method. EEG signals are converted to distance matrices by projecting them on the shapelets' space. Bi-LSTM, SVM, CNN, and an ensemble of them are used to classify the feature set. Based on the prediction results then raise alarms. The proposed methodology is applied to the CHB-MIT scalp EEG dataset of 10 cases. The proposed method achieves a sensitivity of 91.33% and a false prediction rate of 0.16 h−1.

Enhancing COVID-19 Diagnosis: A Multi-Modal Approach Utilizing the CNN Algorithm in Automated Applications

Rapidly identifying COVID-19 patients is essential for effective disease control and management. To address this need, we have developed an automated application that utilizes multi-modal data, including Chest X-ray, Electrocardiogram (ECG), and CT scan images, for precise COVID-19 patient identification. This application comprises a two-stage process, starting with a web-based questionnaire and then the submission of medical images for verification. Leveraging various ML and DL techniques, including CNN, KNN, Logistic Regression, Decision Tree, and NaiveBayes, We conducted extensive model training and validation for LSTM, InceptionV3, SVM, Resnet, and MobileNet. The CNN algorithm emerged as the top-performing method across all modalities, demonstrating exceptional accuracy, precision, recall, F-score, and a minimal false prediction rate. Confusion matrices were employed for comprehensive result evaluation. This study highlights the potential of multi-modal data analysis, particularly the CNN algorithm, for efficiently and accurately identifying COVID-19 patients.

Epileptic Seizure Prediction Using Attention Augmented Convolutional Network.

Early seizure prediction is crucial for epilepsy patients to reduce accidental injuries and improve their quality of life. Identifying pre-ictal EEG from the inter-ictal state is particularly challenging due to their nonictal nature and remarkable similarities. In this study, a novel epileptic seizure prediction method is proposed based on multi-head attention (MHA) augmented convolutional neural network (CNN) to address the issue of CNN's limit of capturing global information of input signals. First, data enhancement is performed on original EEG recordings to balance the pre-ictal and inter-ictal EEG data, and the EEG recordings are sliced into 6-second-long EEG segments. Subsequently, EEG time-frequency distribution is obtained using Stockwell transform (ST), and the attention augmented convolutional network is employed for feature extraction and classification. Finally, post-processing is utilized to reduce the false prediction rate (FPR). The CHB-MIT EEG database was used to evaluate the system. The validation results showed a segment-based sensitivity of 98.24% and an event-based sensitivity of 94.78% with a FPR of 0.05/h were yielded, respectively. The satisfying results of the proposed method demonstrate its possible potential for clinical applications.

Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance.

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Combining temporal and spatial attention for seizure prediction.

Approximately 1% of the world population is currently suffering from epilepsy. Successful seizure prediction is necessary for those patients. Influenced by neurons in their own and surrounding locations, the electroencephalogram (EEG) signals collected by scalp electrodes carry information of spatiotemporal interactions. Therefore, it is a great challenge to exploit the spatiotemporal information of EEG signals fully. In this paper, a new seizure prediction model called Gatformer is proposed by fusing the graph attention network (GAT) and the Transformer. The temporal and spatial attention are combined to extract EEG information from the perspective of spatiotemporal interactions. The model aims to explore the temporal dependence of single-channel EEG signals and the spatial correlations among multi-channel EEG signals. It can automatically identify the most noteworthy interaction in brain regions and achieve accurate seizure prediction. Compared with the baseline models, the performance of our model is significantly improved. The false prediction rate (FPR) on the private dataset is 0.0064/h. The average accuracy, specificity and sensitivity are 98.25%, 99.36% and 97.65%. The proposed model is comparable to the state of the arts. Experiments on different datasets show that it has good robustness and generalization performance. The high sensitivity and low FPR prove that this model has great potential to realize clinical assistance for diagnosis and treatment.

An explainable artificial intelligence approach for mud pumping prediction in railway track based on GIS information and in-service train monitoring data

Automatic and timely identification of mud pumping is important for the reliability and safety of railroads. The current mud pumping prediction model is based on monitoring the dynamic response of railway tracks. The essential geotechnical trigger factors such as the hydrological conditions are not well-considered in these prediction models, as that information is hard to be quantified, which unavoidably reduces the accuracy of the prediction. This paper proposes to utilize the Geographic Information System (GIS) to quantify the hydrological information along railway tracks. Through GIS analysis, the hydrological variables including elevation, near-river distance, rainfall, sink depth, and soil types are estimated and combined with in-service train monitoring data for model development. To deal with multi-attribute data, a dual-channel neural networks model is proposed to separately mine the characteristics in different attributes data for prediction. To further understand the prediction model, Shapely addictive explanations (SHAP) method is applied to estimate the importance of hydrological variables and reveal the possible relationships between the variables and the probability of mud pumping. The proposed approach is applied to a real-life case from the railway tracks in Australia to validate its effectiveness. The prediction results show that the proposed approach can predict mud pumping with balanced accuracy of 90.84%. The results confirm that integrating of GIS information and monitoring data can generate more accurate prediction and reduce the false prediction rate. Based on the explainable results, it is observed that rainfall is the most important hydrological variable that influences mud pumping occurrence. Apart from rainfall, groundwater-related variables show a greater impact on mud pumping occurrence than surface water-related variables. The explainable results also can help infrastructure managers to identify the most vulnerable sections in railway tracks, which facilitates targeted maintenance planning and track substructure design.

Epileptic seizure prediction based on multiresolution convolutional neural networks

Epilepsy withholds patients’ control of their body or consciousness and puts them at risk in the course of their daily life. This article pursues the development of a smart neurocomputational technology to alert epileptic patients wearing EEG sensors of an impending seizure. An innovative approach for epileptic seizure prediction has been proposed to improve prediction accuracy and reduce the false alarm rate in comparison with state-of-the-art benchmarks. Maximal overlap discrete wavelet transform was used to decompose EEG signals into different frequency resolutions, and a multiresolution convolutional neural network is designed to extract discriminative features from each frequency band. The algorithm automatically generates patient-specific features to best classify preictal and interictal segments of the subject. The method can be applied to any patient case from any dataset without the need for a handcrafted feature extraction procedure. The proposed approach was tested with two popular epilepsy patient datasets. It achieved a sensitivity of 82% and a false prediction rate of 0.058 with the Children’s Hospital Boston-MIT scalp EEG dataset and a sensitivity of 85% and a false prediction rate of 0.19 with the American Epilepsy Society Seizure Prediction Challenge dataset. This technology provides a personalized solution for the patient that has improved sensitivity and specificity, yet because of the algorithm’s intrinsic ability for generalization, it emancipates from the reliance on epileptologists’ expertise to tune a wearable technological aid, which will ultimately help to deploy it broadly, including in medically underserved locations across the globe.

An end-to-end seizure prediction approach using long short-term memory network.

There are increasing epilepsy patients suffering from the pain of seizure onsets, and effective prediction of seizures could improve their quality of life. To obtain high sensitivity for epileptic seizure prediction, current studies generally need complex feature extraction operations, which heavily depends on the artificial experience (or domain knowledge) and is highly subjective. To address these issues, in this paper we propose an end-to-end epileptic seizure prediction approach based on the long short-term memory network (LSTM). In the new method, only the gamma band of raw electroencephalography (EEG) signals is extracted as network input directly for seizure prediction, thus avoiding subjective and expensive feature design process. Despite its simplicity, the proposed method achieves the mean sensitivity of 91.76% and false prediction rate (FPR) of 0.29/h on Children's Hospital Boston-MIT (CHB-MIT) scalp EEG Database, respectively, when identifying the preictal stage from the EEG signals. Furthermore, different from traditional methods that only consider the classification of preictal and interictal EEG, we introduce the postictal stage as an extra class in the proposed method. As a result, the performance of seizure prediction is further improved, obtaining a higher sensitivity of 92.17% and a low FPR of 0.27/h. The mean warning time is 44.46 min, which suggests that sufficient time is reserved for patients to take intervention measures by this prediction method.

Predictive data mining models in the tests of propelling charges

In the article, in the introduction, the concept of predictive data mining models and was defined and the purpose of the article was specified. Then, the method of building predic-tive models was characterized and the elements of ammunition were indicated, the test results of which were prepared for the building of models, and the types of ammunition in which the propellant charge is present were indicated. The results of building four data mining models are presented. Predictive models for C&RT, CHAID and exhaustive CHAID decision trees were designed and built. The fourth model analyzed was the SANN model, i.e. the model of neural networks. For each of the tree models, a schema of the designed tree, the rate of false predictions and the parameters of goodness of fit of the built models are shown. For the SANN model, the parameters of the selected neural network were addi-tionally characterized. An analysis of the built models was made and, based on the ob-tained results, the best designed predictive data mining model was indicated. At the end, the graphical form of the workspace predefined by the GC Advanced Comprehensive Classifiers project is shown.

An intelligent optimized deep learning model to achieve early prediction of epileptic seizures

Seizure prediction from electroencephalogram (EEG) time series data and a sequential deep learning (DL) predictor substantially boosts epileptic patients’ quality of life. However, a significant challenge is a variation in seizure characteristics with time and individuals along with a need for more data. Also, considerable dissimilarity is noticed in the duration between various seizure stages. Thus, a patient-generic approach is required to mitigate the problem. As a result, multiple feature augmentation procedures are used to create a hybrid feature space to capture the non-linearity of epileptic seizures. This elaborate feature space helps the predictor learn better to enhance the seizure prediction. Additionally, the predictor is optimized using a novel hybrid Forensic-based-Search-and-Rescue Optimization (FB-SARO) to improve the seizure prediction. In addition, an optimal seizure prediction horizon (SPH) is also determined through the classifier’s learning. The SPH helps attain early prediction while preserving accuracy and achieving a minimum False Prediction Rate (FPR). It also helps raise the alarm to provide the patients with ample preparation time for medical assistance. The proposed approach is testified through publicly available datasets and compared with existing state-of-the-art techniques.

Design a Secure Routing and Monitoring Framework Based on Hybrid Optimization for IoT-Based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have employed in recent years for many different applications and functions. But, it has the critical task to detect the malicious node because node malicious attacks are dangerous attacks, and the concept of a malicious attack is opponents enter the network, search accidentally, and capture one or more normal nodes. A lot of research developed to overcome this problem, but no precise results are found. In this paper, design a Hybrid Vulture and African Buffalo with Node Identity Verification (HVAB-NIV) model to predict the malicious nodes in the WSN. The fitness functions of the HVAB-NIV have operated to recognize the energy level of each node and improve the performance of node detection. The developed replica includes three stages that monitor each node, calculate the energy level and detect the malicious node. More than 100 node inputs were initialized in the proposed technique and implemented in the MATLAB tool. The suggested mechanism enhances the performance of malicious node detection and gains good accuracy for detecting nodes also, it saves running time and power consumption. The experimental results of the developed model has validated with other existing replicas to running time, False Prediction Rate (FPR), detection accuracy, True Prediction Rate (TPR), and power consumption. The developed methods achieve better results by gaining a high rate of accuracy detection, less running time, and false rate detection.