FIELD OF THE INVENTION
This invention relates to the healthcare & more particularly this invention
elaborates the Machine learning & IoT technologies.
BACKGROUND OF THE INVENTION
Several IoT healthcare sensors are combined to support various IoMT
healthcare applications. Future connectivity and reach ability boost IoT,
generating vast data. Billions of IoT devices emit enormous data streams
during processing. Cloud computing offers processing and scalable ondemand storage. Accurate data collection is one challenge preventing
healthcare providers from giving real-time services. Dengue, which
damages the CNS and spinal cord, is a major public health concern.
Superior health monitoring systems are needed worldwide. Dengue is a
virus carried by mosquitos in tropical and subtropical areas. Dengue virus
serotypes can attack a guy four times. In Asia and Latin America, dengue
can be fatal. Since there is no therapy for dengue, these persons must be
constantly watched. If dengue patients are diagnosed early, they can
receive effective medical care, reducing fatalities by 1%. Each year, 100–
400 million dengue infections are discovered. The disease can be treated
with proper precautions.
SUMMARY OF THE INVENTION
This article described CNN-TLSTM with ATLBO for identifying and
preventing Dengue. The suggested model has lower MSE, MAPE, and
overall accuracy than LSTM, RNN, CNN, MLP, and CNN-LSTM.
Variable amount of health records affects training and testing times of
0.034 and 0.024 s. Several validation simulations compare experimental
results to state-of-the-art approaches including I-SCSM, EEFCS, DNN,
and 1D-CNN. The simulation results revealed that the proposed strategy is
more effective and better. Accuracy, recall, precision, and F-score are used
to measure performance. Different statistical parameters show the model's
efficiency. We designed a healthcare model to diagnose dengue regardless
of gender, age, or ethnicity. Future plans include adding new disease related metrics and improving patient credential security.

BRIEF DESCRIPTION OF THE INVENTION
an IoT-aware student-centric stress monitoring (I-SCSM) model to predict the student stress index. At the fog layer, the physiological readings collected from medical sensors with the stress event are classified as normal or abnormal through Bayesian Belief Network (BBN). Based on different stress-oriented parameters at the cloud layer, the time-enriched dataset sequence analyzes abnormal temporal structural data. Further, they formed a multi-level Temporal Dynamic Bayesian Network to calculate the stress index of students. The accuracy and utility of BBN are superior to conducting investigations on both cloud and fog layers. But, it takes larger storage space and unsecured communication. For a large-scale IoT-based healthcare application, proactive personalized services via fog-cloud computing. In data stream processing, an advantage of complex event processing (CEP) was leveraged. For personalized service to reduce resource waste and accelerate response time, they proposed a hierarchical fog-cloud computing CEP structure. Depending upon this structure, the prototype system called FogCepCare was implemented and it demonstrated higher performance results than other conventional techniques. This model is a large-scale- IoT-based healthcare application with a complex structure. For healthcare, Gupta et al. suggested the energy-efficient fog-cloud-based structure (EEFCS).
Usually, the energy consumption increased because of the huge workload on cloud data centers. This architecture does not include any security protocols due to reduce the system complexity. For hospitals and medical enterprises, the partitioning and scheduling of deep neural network (DNN) based applications via IoT-assisted mobile fog cloud were suggested by Lakhan et al.. This design has better energy consumption and accuracy results. Nevertheless, they only considered the complex mobility features with a dynamic environment and there was a higher delay as well as service cost.
IoT-fog-based cloud architecture for smart systems was introduced by Kallel et al. for COVID-19 monitoring. The IoT-aware business process modeling was enabled via the extension of the business process model and notation (BPMN). Further, the experimental analysis showed the enhanced performances in case of entire system reliability, computational time, and guaranteed data integrity. But, the overall performance is degraded due to the system production time improvements, reliability, and security. Based on Fog-cloud environments, a one-Dimensional CNN (1D-CNN) approach was established by Cheikhrouhou et al.for ECG arrhythmia analysis. Over the Fog infrastructure, deploy the inference module of the 1D-CNN.
While applying the Grid Search algorithm, the MIT-BIH Arrhythmia database with the proposed model demonstrated better performances of F1-measure and accurate results with minimum response time but it met a few limitations such as no latency-sensitive health application, additional delay, and no security model. Even though there is different research conducted for dengue disease prediction, the accuracy rate still needs to be enhanced creating room for improvement. This is mainly because when dengue is not predicted in time, it may impact the bone marrow which is the main platelet producing center of the body. Hence this paper presents a novel dengue prediction model to offer real-time disease prediction services to the patients in the remote areas.
FORMULATION OF HYBRID CNN-TLSTM WITH ATLBO ALGORITHM
In this section, we formulate the Hybrid Convolutional Neural Network-based Tanh Long Short Term Memory (CNN-TLSTM) with Adaptive Teaching Learning Optimization (ATLBO) algorithm, which is explained as follows:

CNN-TLSTM Model: This section constructs the Convolutional Neural Network-based Tanh Long Short Term Memory (CNN-TLSTM) model. Figure 1 describes the CNN-TLSTM architecture. The input and output layers with TLSTM layer and CNN layers are present in this model. The relevant data input for classification is the features from the convolution layer which is reduced by the pooling layer. Next to feature extraction, a time series prediction on the input data is performed by the TLSTM layer thereby computing the final output at the output layer.

CONVOLUTIONAL NEURAL NETWORK (CNN): The classification and prediction tasks are accurately performed using the CNN model. Fur- ther, CNN tackles the issues in feature extraction based on artificial neural networks. The predicted value accuracy is affected based on the feature extracted by the CNN and the core of CNN is the convolutional layer. After the convolutional layer, perform the convolu tion via the pooling layer. The overfitting, as well as network parameters, are reduced. In the overall CNN, the fully connected layer is considered as the classifier.

TANH LONG SHORT TERM MEMORY (TLSTM): The LSTM model structure is improved using the new model called Tanh Long Short Term Memory (TLSTM). From overfitting, the input gate in the LSTM output value is prevented by designing TLSTM. Next to the LSTM input gate, the 1-tanh function is introduced and it retains the significant features of the input data. The sigmoid function activates the LSTM input gate and discards the output values close to zero. The output value close to one is trans- ferred and preserved. The correlation among the time series data is captured by transforming data into several distinct intervals. Figure 2 delineates the architecture of TLSTM.

The following equation explains the formulas of TLSTM.

It = s wi • Ht-1, yt + ai (1)
Ot = s wO • Ht-1, yt + aO (2)
Ft = s wF Ht-1, yt + aF (3)
tt = 1 - tanh(It ) (4)
D~ = tanh w • H , y + a
Dt = Ft * Dt-1 + tt * D~ (6) (5)
Ht = Ot * tanh(Dt ) (7)

From the above equations, the input gate weights, forget gate bias, and forget gate weights are wi , aF and wF . The bias output gate, output gate weights, and candidate memory cell bias are aO , wO and ad . Where, wd and aI are the candidate cell weight and the input gate bias. Obtain the relevant values based on the previous TLSTM unit in which the candidate memory cell, forget gate, input, and output gate are entered via the input data of the current moment. After that, the output value of the input gate is transformed via the 1-tanh function. Finally, calculate the present memory cell state, previous TLSTM memory cell state, candi- date memory cell state, transformed output value of the input gate, and candidate memory cell state. According to the output gate value and the current memory cell state, calculate the output value of TLSTM.
ADAPTIVE TEACHING–LEARNING-BASED OPTIMIZATION (ATLBO) ALGORITHM
The superior student considers one student in a realistic class. The student has an active learning ability and good self-learning capacity. Here, we discussed the adaptive teaching–learning-based optimization (ATLBO) algorithm regarding the actual ‘teach- ing–learning’ situation that reveals the higher convergence speed and better solution quality when compared to the TLBO algorithm. The teaching and learning phases are two major stages of the ATLBO algorithm. The following section explains the ATLBO algorithm.

TEACHING STAGE: The influence effects of the teacher on learners inspire the conventional TLBO algorithm. In different classes, consider two teachers teaching subjects. Teacher T1 demonstrates optimal results while teacher T1 performs better than teacher T2. In the instructor phase of the TLBO, the mark is extremely important for enhancing individual marks. When the fitness value of jth students falls below the mean mark for the minimal optimization issue, consider them superior. Equation updates the obtained knowledge from self-study and best individuals.
Ynew, j = Yold, j + (random - 0.5) × 2 × Ymean - Yold, j × ?1 + D × ?2, (8)
, if F Yold, j > F(Ymean)
Ybest - Yold, j × random, if F Yold, j < F(Ymean) (9)

From this, the mean fitness value F(Ymean) is less than the old fitness value F(Yold ). At the starting stage, the student receives knowledge from his teacher. The mean mark and the inertia weights are Ymean and M . In the initial steps, the student’s knowledge is improved via teachers. Equation (10) shows the updating mechanism.

M = ?start - (?start - ?end ) × iteration (10)

The maximum number of iteration is terationmax with the current iteration (iteration). Linearly descend the inertia weight from ?start to ?end . At the initial steps, the search spaces are explored by allowing the ATLBO algorithm to adjust the inertia weight process. The convergence speed is accelerated by introducing ?1 and ?2. At the latter steps, the population diversity is increased to avoid trapping into the local optimum. The student’s knowledge is improved with the important role. The convergence speed and the solution qualities are enhanced via two inertia weights regarding the analysis.
The vital acquisition element of IoT technology is integrated using several real-time appli- cations. The massive data extraction, health-related data management, processing, and data storage are performed using IoT. The cost-effectiveness, storage capacity, accessibility, and scalability are provided via cloud computing. The remote patient health monitoring system is realized based on the healthcare agencies. The overall block diagram of the proposed method is illustrated in Fig. 4. Several numbers of medical sensors and IoT sensor-based devices provide the data for data acquisition. The infected patients are identified and also the details are forwarded to the fog layer. After that, the data classification is performed using a hybrid CNN-TLSTM with ATLBO algorithm and it generates an alert message.
The detailed process of the proposed work is discussed as follows: The hybrid CNN-TLSTM with the ATLBO algorithm is the one that is accommodated for dengue disease prediction. Based on the patient’s historical data stored in the cloud, the proposed model identifies the health state of a person. The results are mainly based on the different disease and environmental parameters such as Soft Bleeding, Muscle Pain, Joint Pain, Skin rash, Fever, Water Site, Carbon Dioxide, Water Site Humidity, and Water Site Temperature. The hybrid CNN-TLSTM architecture mainly depends on three stages: disease monitoring, training, and prediction. The hybrid CNN-TLSTM architecture assigns unique values for each parameter based on their importance. The probabilistic values are mainly determined by the domain experts. The monitoring process mainly happens based on the computational capability of the fog layer and it stores the parameters based on the temporal data.

DATA ACQUISITION: In real-time from the different sensors, the health-related data is collected using the data acquisition layer [23]. The remote devices transit the acquired information that is responsible for data analysis and it acts as the fog node. Over the wireless network, transmit the data for in-depth analysis and detailed processing. Address the security and privacy concerns of IoT networks in addition to data accumulation. Effective data security is provided with several protocols. All data to and from IoT devices must be encrypted using stronger data security methods such as the Elliptic Curve Cryptography (ECC) algorithm and the Secure Socket Layer (SSL) protocol. Over the Message Query Telemetry Protocol (MQTP) and Hypertext Transfer Protocol (HTTP), these protocols guarantee secure communication. The IoT devices are implemented in a software-level platform with an easy-to-use Application Programming Interface (API). The Amazon Web Services (AWS) architecture allows for increased efficacy and accuracy in data storage. Every data value is subjected to real-time processing at the fog layer. If an intrusion is discovered, the system processes all data values in real-time.

HYBRID CNN-TLSTM WITH ATLBO ALGORITHM FOR DATA CLASSIFICATION AND VISUALIZATION: It is important to formulate the data into multiple groups after the heterogeneous data is accumulated from various sensor devices. For better classifications, apply the extraction techniques because of data heterogeneity. Disorientation, Coma, Joint pain, Paralysis, Nausea, Vomiting, and High fever are the vital symptoms present in the health data. These kinds of data are collected by using different health-related medical sensors. The data analysis is required to detect health-related vulnerability. The fog layer detects patients using the health state categorization components.
The real-time analysis of heterogeneous data is carried out at multiple time slots (ot) to use fog computing nodes. The fog layer detects the patients by using the health state categorization components. In this study, we used hybrid CNN-TLSTM with the ATLBO algorithm (as discussed in Sect. 3) for dengue disease data prediction and classification. The major task is Spatio- temporal detection in the fog layer. An event in time data value is stored on a primary basis. The event manifestation is assigned to the Spatio-Temporal Granulation [26]. The proposed hybrid CNN-TLSTM with the ATLBO model classifies the identical data instances into two classes which denotes the presence or absence of dengue illness based on the dengue accumulated features. While taking the dataset partition, the cost function of hybrid CNN- TLSTM with ATLBO reduces the error [27]. Based on the classification results, 0 means the absence of dengue, and 1 is for the presence of dengue. Finally, the patient’s result is sent to their mobile devices as an alert message.
The health attributes based on dengue fever are explained in Table 3. The environmental attributes like temperature, carbon dioxide, humidity, and mosquito-dense sites are captured and deployed in a total of 1240 sensors in different locations. To generate environmental attributes random values, the send script program these sensors. The attribute value, Sensor ID, and sensor location are present in the environmental dataset. From this, the symptoms of Yes are referred to as Y and No are denoted as N .
During simulation, different parameters were altered for tuning. The health-related parameters of the patients are collected via the IoT devices which are interconnected with different devices for healthcare data transmission. The application also provides a form for the patients to enter if they feel any abnormalities such as soft bleeding, muscle pain, joint pain, skin rash, severe abdominal pain, vomiting, pain behind the eyes, severe headache, etc. With these details, the patient’s health conditions are sent to edge computing for processing. The physical movements of the patients are also analyzed by their wearable devices. These data gathered are then preprocessed by the Hybrid model. To generate environmental attributes random values, the send script program these sensors. The attribute value, Sensor ID, and sensor location are present in the environmental dataset. From this, the symptoms of Yes are referred to as Y and No are denoted as N .

The missing values are rectified via the imputation and the TLSTM technique. The noise is minimized to improve the detection accuracy and the data is mainly analyzed in terms of rows and columns. To minimize the complexity associated with the diagnosis process, the normalization takes place in the 0 to 1 range for multiple data distribution. The data is obtained from a total of 1500 patients in the rural areas via different sensing gadgets such as smart watches, mobile phones, etc. Here 70% of data is taken for training and the remaining 30% is taken for testing. For each parameter, their associated threshold value is computed based on their symptoms. There are a total of 6 environmental-related symptoms and 10 health-related symptoms with a label yes or no. We generate the synthetic dataset by allocating the probability value to the health and environmental-related attributes. The probabilities generated for the different dengue symptoms are presented.

We Claims:

1. Even though there is different research conducted for dengue disease prediction, the accuracy rate still needs to be enhanced creating room for improvement.
2. The massive data extraction, health-related data management, processing, and data storage are performed using IoT. The cost-effectiveness, storage capacity, accessibility, and scalability are provided via cloud computing.
3. Nevertheless, they only considered the complex mobility features with a dynamic environment and there was a higher delay as well as service cost.
4. The hybrid CNN-TLSTM architecture assigns unique values for each parameter based on their importance. The probabilistic values are mainly determined by the domain experts.
5. The Amazon Web Services (AWS) architecture allows for increased efficacy and accuracy in data storage.
6. Address the security and privacy concerns of IoT networks in addition to data accumulation. Effective data security is provided with several protocols.
7. These data gathered are then preprocessed by the Hybrid model. To generate environmental attributes random values, the send script program these sensors.