Diagnosing Parkinson's disease (PD) in its early stages, particularly in older adults, remains a challenge due to the complexity of symptoms and their overlap with other age-related conditions. To improve early diagnosis, our study introduces the SymptoSense Model, which integrates advanced Natural Language Processing (NLP) with ocular imaging techniques to accurately predict PD symptoms from patient-generated text. Leveraging a corpus developed by the Michael J. Fox Foundation for Parkinson's Research and the Institute for Clinical Evaluative Sciences, the model creates a patient-specific dictionary by correlating segmented words from patient responses with predefined standards. Additionally ocular imaging features of microvascular changes of retina and abnormalities in eye movements patterns are investigated to enhance prediction accuracy. Combining NLP and ocular imaging, this innovative approach is evaluated against benchmark models like Forward Maximal Matching, Backward Maximal Matching, Bi-directional Maximal Matching, Word Embeddings, Sentiment Analysis, and Term Frequency-Inverse Document Frequency. The SymptoSense Model shows superior performance, achieving 94.2

Smart cities, driven by technological advancements, face challenges related to environmental pollution, including poisonous gas emissions. Existing systems often struggle to efficiently monitor and predict these emissions, leading to limitations in accurately assessing and mitigating air quality issues. This study proposes a groundbreaking solution, the Novel Temporal Dynamics Prediction (NTDP) model, designed to overcome the limitations of current systems. By harnessing the NTDP model’s innovative approach, smart cities can enhance their capability to analyze and forecast poisonous gas emissions, thereby improving the effectiveness of environmental management. The NTDP model offers a promising avenue for the future, revolutionizing the way smart cities address and mitigate the impact of toxic pollutants on air quality. The NTDP model achieved an accuracy of 99.1%, sensitivity of 98.9% and RMSE training 1.6 and testing 1.54 ug/m3. The results affirm the robustness and effectiveness of our optimized implementation, positioning it as a standout solution in disease prediction compared to commonly used machine learning techniques.

It is well-known that nanofluids differ significantly from traditional heat transfer fluids in terms of their thermal and transfer characteristics. Two of CO2 transfer characteristics, its thermal conductivity and its viscosity, are crucial to improved oil retrieval methods and industries refrigeration. By combining molecular modelling with various machine learning algorithms, this study predicts the conduction characteristics of iron oxide CO2 nanofluids. It is possible to evaluate the accuracy of these transfer parameter estimates by applying machine learning methods such as decision tree, K -nearest neighbors, and linear regression. Predicting these transfer qualities requires knowing the size, fraction of nanoparticle volume, and temperature. To determine the characteristics, molecular dynamics simulations are run using the large-scale atom Vastly equivalent simulant. An inter- and intra-variable Pearson correlation was established to confirm that the input variables were reliant on m and thermal conductivity. The results were finally confirmed by using statistical coefficients of determination. For a variety of temperature ranges, volume fractions, and nanoparticle sizes, the study found that the decision tree model was the best at predicting the transport parameters of nanofluids. It has a 99% success rate.

This study delves into the Smart Marketing approach, particularly using Novel Global Vectors for WordRepresentation (GloVe) on a selected dataset. It also offers a comparison with the BOOMSOONARalgorithm. The effectiveness of the Smart Marketing strategy was assessed based on accuracy. With a samplesize of 22, both the Novel GloVe and BoomSonar algorithms were assessed, utilising G power calculated atan 80% power level. Although the Novel GloVe algorithm displayed an accuracy rate of 77.45%, it wasmarginally overshadowed by BoomSonar's 78.05%. However, statistical evaluations suggest no significantvariance between the two. The p-value stood at 0.886, suggesting the mean accuracy for both algorithms fellwithin a 2-standard deviation range. Thus, in terms of Smart Marketing, while the Novel GloVe hadcommendable accuracy, BoomSonar slightly edged it out.

The goal of research is to use the Novel YOLO V3 SPP for detecting malicious applications while comparingit with the OCR technique for computation of access time. Materials and Methods: The Innovative YOLO V3SPP algorithm is used to determine access time using a sample size of (N=25), a total sample size of (N=50),and G power is computed to be 80%. In terms of data exploitation prediction, the Novel YOLO V3 SPP hasan access time that is slower (83.36ms) than the OCR algorithm's (79.64ms). According to the results, thereis no statistically significant difference between the Novel YOLO V3 SPP Algorithm and the OCR Algorithmwith p=0.218 (independent sample t-test p<0.05). In comparison to OCR's access time of 79.64ms, the novelYOLO V3 SPP method predicts vulnerabilities in native programmes with a longer access time of 83.36ms.

Aim: The primary importance of this research is to generate the comparison of a precise parameter elimination method in Ensemble understanding on NSL-KDD dataset in comparison with Candidate Elimination Algorithm. Materials and Methods: Accuracy is analyzed for feature elimination. Classification of Novel feature elimination is experimented by ensemble model of sample size (Number of samples=34) and Candidate Elimination model of (Number of samples=34) which produced with the G-power value 80%. Results: Ensemble model has achievement of 82.74% accuracy which has higher than Candidate Elimination Algorithm proves its accuracy of 74.38%. The significance results of accuracy is 0.095 p less than 0.05 indicates the performance of proposed work has insignificance. Conclusion: Ensemble model is better in finding accuracy of 82.74% when refers to comparison of Candidate Elimination Algorithm of accuracy 74.38% along with the hypothesis area to determine the set of features.

Aim: For contrasting the accuracy of understandability between the techniques Novel Rapid Automatic Keyword Extraction (RAKE) and BOW (Bag Of Words) in the NLP (Natural Language Processing) utilizing the corpus Donald trumph speeches for building squarified charts from big text, that is the central goal. Materials and Methods: After pulling off key phrases from the huge content in a document, the rate of accuracy is evaluated. The accuracy rate evaluated based upon the quantity of similar key phrases in Novel RAKE and BOW compared with manual allotted key phrases. The key phrases grabbing done utilizing the Novel RAKE and BOW with sample sizes each 28 in the NLP (Natural Language Processing) resulting in G-power value 80%. Results: The Novel Rapid Automatic Keyword Extraction (RAKE) shows a larger rate of accuracy of 73.04% than the BOW (Bag Of Words) of 69.04% in gathering key phrases from a text file. This indicates that there is no statistically significant difference between the Novel RAKE algorithm and the BOW method with $\mathrm{p}=0.333(\mathrm{p} > 0.05)$ . Conclusion: The Novel RAKE has a larger rate of prediction accuracy of 73.04% when distinguished with the rate of prediction accuracy of 69.04% in BOW and subset of parameters or attributes in the marginal distribution.

Cyber security is the safest way to protect the data from hackers and unauthorized users. Healthcare technologies nowadays face lots of cyber security issues related with the security of the health information and privacy of the data. Cyber security is one of the popularized ways to protect the data from hackers and spammers. HealthCare is the field where the data is highly sensitive and the security for the systems is low. Protecting the sensitive data is achieved by the blockchain method, which is similar to a database but the difference is the data stored in the blockchain in blocks. The new blocks included are connected to previous blocks from a chain like structure, it is very secure, each block stores data and also the hash of previous blocks. So, data cannot be easily accessed or manipulated. The mechanism used in blockchain for the security of the data consensus mechanism which contains the different methodologies includes Proof of Work (PoW), Proof of Stake (PoS), Proof of Space and Proof of Authority. Enhanced proof of stake is a combination of the PoS and DPoS used to increase the security of the system by eliminating the 51% attack in blockchain and reduces the data theft threats and protects the medical records of patients from hackers.

Aim: This research aims to predict the vulnerabilities in native applications using the Novel YOLO V3 SPP algorithm in comparison with YOLO algorithm to compute access time. Materials and Methods: Access time is calculated using the Novel YOLO V3 SPP algorithm of the sample size of (N=25) with the total sample size is (N=50) and G power is computed to be 80%. Results: Novel YOLO V3 SPP has access time which takes more time (83.36ms) than the YOLO algorithm has (79.72ms) in data exploitation prediction. There is no statistical significance difference among Novel YOLO V3 SPP with YOLO Algorithm with p=0.239 (Independent sample t-test p<0.05). Conclusion: Novel YOLO V3 SPP algorithm has a more access time of 83.36ms compared to YOLO algorithm access time of 79.72ms in predicting vulnerabilities in native applications.

A wireless ad hoc network which is infrastructure less where the communication of the mobile nodes is done through wireless mode without any base station is termed MANET (Mobile Ad hoc Network). One of the main issues of MANETs is routing due to the free movement of nodes and changing topology. As a result of this, there is a change in topology due to failure in the routes thereby decreasing the network performance. Forwarding of data between the transmitter and the receiver is the main aim of routing. Node connection can be made at any time and data can be forwarded to the specifically selected nodes. There is a need for developing an effective routing protocol to find the shortest path for packet forwarding. The proposed work employs cuckoo search algorithm along with destination sequenced distance vector to identify routing paths that are shorter to forward the data packets. In order to perform validation, the proposed work is compared with ad hoc on-demand distance vector routing protocol to show the efficacy of the system. The packet delivery ratio of the proposed system is high which makes the DSDV protocol to be suitable for various communication.

Predicting stock prices in the online smart market is a complex task, and leveraging advanced data mining techniques has become essential for accurate forecasting. This study proposes a novel approach utilizing an ensemble neural network combined with swarm optimization for enhanced predictive accuracy. The ensemble neural network, a robust machine learning approach, is adept at capturing complex patterns in stock market data. Concurrently, swarm optimization further refines the model's predictive capabilities, optimizing parameters for superior performance. By incorporating these techniques, the study unveils future trends in predicting online smart market stock prices, providing investors and traders with invaluable insights for informed decision-making. Existing algorithms are limited. The ensemble neural network integrates diverse models to capture intricate patterns in financial data, while swarm optimization refines the model parameters for optimal performance. The experimental results showcase an impressive accuracy of 92.5%, highlighting the efficacy of the proposed methodology. This research not only contributes to the field of stock price prediction but also provides valuable insights into future trends in the online smart market.

Small MGS (microgrid systems) are capable of decreasing energy losses. Long-distance power transmission lines are constructed by integrating distributed power sources with energy storage subsystems, which is the current trend in the development of RES (renewable energy sources). Although energies produced by RES do not cause pollution, they are stochastic and hence challenging to manage. This disadvantage makes high penetration of RES risky for the stability, dependability, and power quality of main electrical grids. The energies obtained from RES must thus be integrated in the best possible way. To provide maximum energy sustainability and best energy usage, hybrid energy systems must manage energy efficiently. In order to improve power management and make better use of RES, this study offers a hybrid energy power management controller based on hybrid MABC (modified artificial bee colony) and ANN (artificial neural network) for MGS, PVS (photovoltaic system), and WT (wind turbine). Controlling power flows between grids and energy sources is the suggested approach for power control. D/R (demands/responses), customer reactions, offering priorities, D/R properties like COE (cost of energies), and sizes (lengths) are considered in this work. Along with current techniques, a suggested model is implemented in the MATLAB/Simulink platform.

Aim: An efficient approach to classifying newspaper articles using a multi-class Support Vector Machine. Materials and Methods: Accuracy stands as result for the classification of text analysis. Factual texts merely attempt to inform, whereas virtual texts try to amuse or combative readers by inventive language and imagination. The rate of correct classification of novel texts is low and classification occurs in the areas of text analysis and classification of multiple articles. The binary classification and the separation of data points into classes. The multiclass SVM is used for splitting the multiple into severely binary classification. The Novel Text Classification is checked by sample size (N = 42) Support Vector Machine obtained with G-Power taking value equal to 80%. Results: Accuracy is the outcome, Support Vector Machine accuracy rate is 82.71%, which is relatively higher than the Binary Classifier (BC) with 71.48%. Significance value accuracy becomes 0.101 (p>0.05). Conclusion: SVM works and gets more accurate than the Binary Classifier. And this research is evaluated to predict accuracy for a system that is proposed Support Vector Machine is higher than existing comparison utilizing Binary Classifiers.

Electronics Medical Records (EMRs) have gained popularity in modern healthcare. Using the EMRs, a Natural Language Processing (NLP) algorithm is used to segregate and process the information. In this proposed work, a novel convolutional neural network with HMM algorithm is introduced to process the information and segregate the same based on the type of information obtained. In this methodology, the initial data is extracted with the help of convolutional neural network which extracts and identifies the letters of the image to further process it. This data is then then fed to a primary symptom dictionary is introduced which is sub-divided into sub-dictionaries based on the length of the words. BMM and FMM are incorporated in an inverse matching direction. The words that are remaining are segmented with the help of HMM technique and correlated with the existing text. The proposed algorithm is further compared with several algorithms that use segmentation. Based on the output obtained, it is identified that the proposed work is efficient in terms of precision, recall metrics and F1 scores attaining 96%, 95% and 93% of accuracy respectively. The proposed algorithm also achieves better performance in terms of observing and classifying symptom text in real-time.

Nowadays, symptoms-based disease prediction is more helpful for predicting the correct type of disease. These predictions are more helpful to the medical industry and doctors; they can reduce the time taken to check up and accurately predict the disease type. Combining the Bidirectional Encoder Representations from Transformers (BERT), Natural Language Processing (NLP), Hidden Markov Model (HMM) and Bi-directional Maximal Matching algorithm (BiMM), a unique word classification algorithm, BERT-BiMM, is presented. It can use quick word segmentation to match sub-dictionaries. The knowledge hidden in many research topics can be found using Natural Language Processing methods. The knowledge hidden in many research topics can be found using NLP methods. The remaining text is joined by the remaining specific words BiMM post text segmentation; the remaining text is connected by the remaining single words using Hidden Markov Model. We compared the real-world clinical text. We took some symptoms of the disease. HMM, BiMM, achieves maximum accuracy. BERT is used to connect the remaining text with these methods, and high accuracy in medical text segmentations will be obtained if this model outperforms provincial models by achieving strict scores of 91.29% on the BiMM model.

Artificial Intelligence (AD is the wide application that learns the problem and features by given data and processes the data like the human brain. When a computer program imitates a characteristic of the human brain that is considered “innovator.” Among the methods are statistical methods, methods for artificial intelligence, and traditional order to verify the validity. The expansion of AI is also related to virtually infinite storage and an abundance of data, including exchanges, geospatial information, video files, photos, text messages, and audio files. Machine learning is divided into deep learning and deep learning is primarily divided into numerous layers of neural networks. This pattern gives it the ability to learn a lot of information and attempt to replicate the brain function. Increasing the efficiency by attaching more covert layers can be beneficial. It is used to gather the data and transfer the data. Aquaculture production has grown into a barrier to the growth of fish culture and the counting operation represents one of the problems experienced during the spawning process. Previous studies have primarily relied on the application of manual and automated counting techniques, which has prevented it from producing accurate results. The proposed method offers a promising method for enhancing image detection by combining the IoT techniques. The image data were divided into three categories: low frequency, intermediate density, as well as high frequency. The proposed method has used 8200 images to train and 2500 images for verification. Only the data relevant data sources were used during the train and verification phase in order to find the proper parameters and create a better VGG19 parameter calibration strategy. Consequently, the improved VGG19 model can achieve an accuracy of 98%.

Indian tribal peoples have some issues in their education. They are differentiated by their looks, appearance and language. This kind of differentiation makes their life in trouble compared with the normal life of other peoples. One of the main reasons for this difference is their language. Language of tribal people cannot be understood by other peoples of India because their language is so different from other languages and it cannot be understood and learned easily from them. Because of this they cannot go to school and do not continue their studies as well. Teaching language is differed from their mother language so the tribal children’s unable to understand what they are teaching and nothing learned from schools. Due to this issue, they cannot continue their studies properly. To overcome this issue, they continue studies in their mother language, nowadays digital technologies play an important role in Education field. Artificial Intelligence based technology is to help tribal people education, and teaching them in their mother language. Substantial CNN approach proposed in this paper. Feature extraction done by spectrogram and audio signal and it performed well by this approach. This approach overcomes the limitations and performs more efficiently than the existing approach. It produced an 85.67% accuracy range.

Education is important to all children but some children have some issues with the education for example tribal children, refugees and differently disabled children. Tribal children face some barriers in their education like language problems, they are in remote areas and economic issues also. One of the strongest reasons for their study issue is language. Language is a bridge for communication but here it is a problem for these tribal students. Because learning and teaching language is different from their mother language. For this reason, they are unable to learn anything from school. Due to this reason most, tribal students discontinue their studies, to overcome this issue they studied with their own language. Artificial Intelligence will help to eliminate this barrier from them. AI based speech recognition systems with text normalization assist them to continue their studies with the mother language of tribal children. To create a text normalizer that helps to educate tribal children with proper transcription of what they are studying. Speech recognition system by using CNN model and Bi-LSTM to create a text normalizer proposed in this paper. This convergence helps to remove grammatical errors, mistakes, time, date and frequency error from the transcription. This method achieves 92.17% accuracy range during test time and it shows better feature performances efficiently.

Life Insurance prediction is the main objective of the research: to evaluate a person’s life insurance using a machine learning model. In day-to-day life human life becomes hectic and the life span of everyone gets reduced due to pandemic situations, unavoidable accidents and historical impacts, etc. Even though the security and saving beneficiaries are there in life insurance, risk factors are also associated with it. Machine learning techniques propose a risk reduction avoidance and prediction of financial scams to save customer’s lives when individual customers claim their own experience in risk factors by sharing their own credentials. The analysis is made by logistic regression based on the probability of categorical data such as identity proof, Aadhar proof, PAN number, and so on as attribute value. Using the novel Two-way cross-tab method the relationships based on attribute value matrix value are generated to find the customer who has no mutual identifier to take the life insurance cash and summarize the prediction using the bivariate exploratory flow of graph. By seeing the difference in the relationship, the threats and risk full factors can be reduced in accuracy compared to existing machine learning models.

Sentimental Emotion Recognition is the recognition of emotion which is detected by many fields such as Artificial intelligence, Machine Learning and Deep Learning. The professionals need sentimental analysis for business like social media monitoring, brand monitoring and customer feedback which will help in business for improvising the product based on the emotion gathered from the customers. Sentimental analysis is used in business to mine the data of customers about how they are feeling about the product. Existing systems like Facebook, WhatsApp and twitter use sentimental emotions for sensing the user’s emotions. In our proposed system we are using the Novel Convolution Neural Network (N-CNN) system for sentimental recognition to make better understanding of customers which can be used to improve the product quality. To enhance the accuracy of the proposed model Amazon review for product sales is used which is available in kaggle. Using the proposed model the model in comparison with existing model the preprocessed feature which are extracted from multiple neural networks are recognized. Feature based on the selected customer feedback on constant steps of filtering, max pooling and necessary activation function results are implemented and shown 98.3% of accuracy which is more reliable results than the other Machine learning model.