Recent Patents on Engineering - Volume 19, Issue 1, 2025

Casson nanofluids are used to investigate the effects of Magneto hydrodynamics (MHD), viscous dissipation, temperature and concentration on convective heat transfer flow through a stretching/shrinking vertical sheet.

The BVP4C method in MATLAB is used to obtain numerical solutions for solving the governing Ordinary Differential Equations (ODEs) by converting them into the governing Partial Differential Equations (PDEs) using similarity transformations. To examine the effects of pertinent variables, including the Magnetic parameter, the Brownian motion parameter, the Cassson fluid parameter, the chemical reaction constant, the Prandtl number, the concentration to thermal Buoyancy ratio, the microorganism to thermal Buoyancy ratio, the Lewis number, the bioconvection Peclet number, the bioconvection Lewis number, the local skin friction, the local Nusselt number, the local Sherwood number and the local density number of the motile microorganisms.

Quantitative data are plotted according to the bioconvection flow, temperature, concentration and velocity profiles.

It is observed that this patent study helps to compare the variations in the chemical reactions of the MHD Casson nanofluid by using graphs. Which in turn also leads to providing a concept of developing a patent over Casson nanofluids.

Land use and Land cover (LULC) are now major worldwide issues. The need for land is growing due to urbanisation and industrialisation, thus to meet this need, forest and vegetation land are transformed to open land that is either utilised for colonisation of urban areas or industrial usage. Patents are done on the calculation of LST.

The study aims to provide a detailed analysis of land and temperature change with variation in Normalized difference vegetation index (NDVI) and normalized difference build-up index (NDBI) for the study area using a geospatial technique. The LULC classification is performed based on four classes which are Bare land, Built-up, Vegetation, and Waterbodies from the year 2000 to 2020. The classified data is further used to extract the Land Surface Temperature (LST) data from the thermal band to generate LST maps. The NDVI and NDBI maps are also generated using the land sat imageries. From the above-mentionedanalysis, it is found that Nagpur city temperature has risen by 3.67°C in two decades. Whereas, LULC results show that bare land and vegetation decreased by 11.88% and 14.93% respectively, while an increase is seen for built-up and water bodies by 25.62% and 0.19% respectively.

Regression analysis between temperature and NDVI, NDBI shows that temperature and NDVI have a negation relation and NDBI has a positive relation with temperature (Pearson’s r: between -0.89 to -0.81 and between 0.90 to 0.81 respectively) for both the years. The increased temperature is a result of urbanization in the study area. The study reveals that for assessment of LULC and LST with the incorporation of GIS and Remote sensing can be effective and swift.

This study recommends that policymakers develop policies that should minimize the transition of different classes and check the outcome of industries and the temperature of the surroundings.

Wireless Body Area Networks (WBANs) are similar to custom Wireless Sensor Networks, so these networks are prone to adversaries through their activities, but in healthcare applications, security is necessary for the patient data. Moreover, providing reliable healthcare to patients is essential, and for the right treatment, correct patient data is required. For this purpose, we need to eliminate anomalies and irrelevant data created by malicious persons, attackers, and unauthorized users. However, existing technologies are not able to detect adversaries and are unable to maintain the data for a long duration while transferring it.

This patent research aims to identify adversarial attacks and solutions for these attacks to maintain reliable smart healthcare services.

We proposed a Convolutional-Bi-directional Long Short-Term Memory (ConvBi-LSTM) model that provides a solution for the detection of adversaries and robustness against adversaries. Bi-LSTM (Bidirectional-Long Short Term Memory), where the hyperparameters of BiLSTM are tuned using the PHMS (Prognosis Health Monitoring System) to detect malicious or irrelevant anomalies data.

Thus, the empirical outcomes of the proposed model showed that it accurately categorizes a patient's health status founded on abnormal vital signs and is useful for providing the proper medical care to the patients. Furthermore, the Convolution Neural Networks (CNN) performance is also evaluated spatially to examine the relationship between the sensor and CMS (Central Monitoring System) or doctor’s device. The accuracy, recall, precision, loss, time, and F1 score metrics are used for the performance evaluation of the proposed model.

Besides, the proposed model performance is compared with the existing approaches using the MIMIC (Medical Information Mart for Intensive Care) data set.

Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures.

The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance.

This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories; feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn.

Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time.

The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels.

Any electronic device that delivers a collection of data, whether it be text referring to full text databases, electronic journals, photographs, other multimedia goods, or quantitative, visualizations, or time-based, is referred to as an electronic resource. These could be transmitted over the internet, tape, CD-ROM, tablets, smartphones, smart watches or another medium, these are now the basis of e-learning. Online searching has made it possible to get patent information more quickly, affordably, and conveniently than the traditional manual or CD-ROM based searching method. The ability to create and distribute documents in electronic form is now made possible by a number of established procedures and standards. So, in order to address the current problems, librarians are utilizing new media, particularly electronic resources, in their collection expansion makes the documentation of users better. As we can see, utilizing online resources is important in the modern world for a multitude of purposes. Because of this, it's important to understand the preferences, motives and usage of various ‘e- resources used by students who use online learning’. The aim of the present research paper was to examine the impact of e-resources using its usage and reading preferences. In this study, reasons such as time saving, more information, and busy schedule at college are considered.

Primary data was gathered from 250 students from Mumbai and Navi Mumbai who are using e-resources through the pre-structured questionnaire. The responses collected were recorded using the SPSS software for data analysis. In order to examine the link between causes, preferences, and the use of ‘e-resources’, a theoretical construct was developed grounded on a few assumptions. Statistical techniques like the chi-square test were used and data analysis was done using SPSS version 20 to examine the proposed construct. When doing the data analysis, the demographic profile, objectives, and hypothesis were all taken into consideration.

The average for each component that is time saving, more informative, and busy schedule at college was computed and was determined as 0.004, 0.004, and 0.000, correspondingly, for time saving, more informative content, and busy college schedule. As all of these values for all of the preferences under consideration are less than 0.05, it is clear that there is a connection between the usage of electronic resources and their underlying reasons and preferences.

Hence, there is a substantial correlation between the reasons for using electronic resources and the different reading preferences, as well as between the two. Only three reasons namely time saving, more informative, and busy schedule at college are considered during this study. Data collection is done from Mumbai and Navi Mumbai region only.

Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant.

This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests.

The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability.

The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model.

Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages.

The application of irrigation water to various crops in the command area based on daily crop water requirements considering the water holding capacity of different irrigated soils is a vital aspect of irrigation management. Considering the importance of irrigation scheduling, the FAO CROPWAT 8.0 is a patent tool, as it gives crop water requirements and irrigation schedules based on climatological and physiographic factors.

In this patent study, the CROPWAT 8.0 model is used to integrate the Cropwat model, long-term climate data is used, soil sensitivity analysis is performed and crop-specific water need is identified for the command area of the Pawale irrigation project which is a novelty as cropwat is not used previously for the study area. Pawale irrigation project is located in the Thane district of Maharashtra, India. Nineteen years of climatic data are used for the analysis, considering seven crops to calculate the crop water and net irrigation requirement for the kharif and rabi seasons.

The result indicates that crop-wise and season-wise variation of crop water requirement is from 2.5 to 1055.1 mm, and the net irrigation requirement for the year is 618.6 mm. It is also observed that rice requires more water from the initial stage up to the development stage than other crops considered in this study.

In conclusion, we can say that the cropwat model with long-term climate data can develop effective data for crop's specific water needs. The results indicate that evapotranspiration has a greater impact on crop water and net irrigation requirements because, in both cases, the increase or decrease of ETo will affect the crops and their water requirement. The sensitivity analysis for different types of soils is also carried out for groundnut. The result indicates that, apart from crops, soil water-holding capacity is essential for irrigation scheduling. It is seen that nine rotations are required for red sandy soil as compared to six rotations and four rotations for red sandy, loamy soil and black clay soil, respectively.

The objective of this study is to estimate the mismatch error between the human knee and implanted knee for total knee replacement with the help of data analysis considering the anthropometric and implant data for the Indian population.

Anthropometric data collected for 150 cases from the hospital was contrasted with the typical implant data from the Johnson & Johnson Company and Zimmer. In the data collected for 150 cases, 91 were female patients and 59 were male patients. The maximum cases were for osteoarthritis and rheumatoid arthritis. For each patient—male and female—the mismatch error was computed separately. Major focus of the study was laid on the femoral condyle.

Zimmer implant mismatch errors were computed as follows: -1.18 for A/P and 4.95 for M/L in patients who were male; -5.6 for A/P and -3.3 for M/L in patients who were female and male. -3.4 for A/P and -0.4 for M/L in female patients; 1.85 for A/P and 8.18 for M/L in male patients was the mismatch error computed for Johnson & Johnson implants. The total discrepancy in implant results was 1.83 for men and -4.4 for women for Zimmer, and 5.01 for men and -1.89 for women for Johnson & Johnson. A mismatch of -19 (for females), -15 (for men) was identified for Zimmer, and -11 (for females), -7 (for males) was found for Johnson & Johnson. The femoral condyle was the cause of several inaccuracies.

On the basis of results from data analysis it was found that female patients were more into pray of high mismatch errors. Also, femoral condyle mismatch was majorly responsible for the improper fitting of implants error. So, a 3-D model was developed using Slicr3r to justify that the gap between the implant and implanted knee must not exceed 2mm for femoral condyle in order to get the best fit. A patent on Asymmetric Prosthetic Tibial Component is available to explain a similar concept.

Vehicular Ad-hoc Network (VANET) is wireless communication between Roadside vehicles and vehicle infrastructure. Vehicle Ad Hoc Network (VANET) is a promising technology that effectively manages traffic and ensures road safety. However, communication in an open-access environment presents real challenges to security and privacy issues, which may affect large-scale deployments of VANETs. Vehicle identification, classification, distribution rates, and communication are the most challenging areas in previous methods. Vehicular communications face challenges due to vehicle interference and severe delays.

To overcome the drawbacks, this patent work proposed a new method based on the Artificial Neural Network Trust Authentication Secure Sugeno Fuzzy System (AN²-TAS²FS). Vehicular Ad Hoc Networks (VANET) are required to transmit data between vehicles and use traffic safety indicators. Improved Cluster-Based Secure Routing Protocol (ICSRP). Artificial Neural Network Based Trust Authentication Secure Sugeno Fuzzy System (AN²-TAS²FS) used the symmetric key to increase the security performance of VANET. Use ANFIS-based Secure Sugeno Fuzzy System for calculating the node weights for data transferring; reduced the attacks accuracy of network malicious attacks.

In the improved cluster-based VANET routing protocol, each node obtains an address using a new addressing scheme between the wireless vehicle-2-vehicle (V2V) exchanges and the Roadside Units (RSUs). It will explore the effectiveness of the Secure Sugeno Fuzzy System-based adaptation term Enhanced Cluster-based routing protocol in finding the vehicle's shortest-path for transmission.

Simulation results show that in the proposed ANN-based Trust Authentication Secure Sugeno Fuzzy System (AN2-TAS2FS) analysis, the packet delivery ratio is 93%, delay performance is 0.55 sec, throughput performance is 94%, bandwidth is 55 bits/sec, Network security is 92%, and the transmission ratio is 89%, attack detection is 90%.

Among the various statistical measures, Root Mean Square Error (RMSE), Mean absolute Percentage Error (MAPE), and R-squared (Coefficient of determination) are the most widely used methods. The significance of the R square approach in the medical field was extensively discussed in the current review. Furthermore, we compared a number of statistical metrics for potential applications in the treatment of various disorders. In addition, the pertinent patents of R square for the consequences of testosterone and the enzymes aspartate dehydrogenase (AST) and alanine transaminase (ALT) on Polycystic Ovary Syndrome (PCOS) treated patients have been developed.

We study in this paper the detailed comparative study on the biological system using RMSE, MAPE, and R Squared, which consists of 29 PCOS-influenced women against 20 healthy women and followed by the obesity verification model over the Sprague Dawley rats.

R Square provides the best results among all mathematical regression analytical methods in PCOS-influenced patients.

In this study, we provide the strong conclusion that aspartate dehydrogenase (AST) with testosterone treated on PCOS influenced women to have a greater chance of getting affected by Non-alcoholic fatty liver disease (NAFLD) rather than alanine transaminase (ALT) with testosterone-treated patients. Furthermore, this study extends their mathematical regression analysis through squared for the obesity verification over rat model. It confirms that letrozole-treated rats are inhibited in obese compared with control rats, which results in a chance of NAFLD. Therefore, AST combined with testosterone creates a major chance for liver dysfunction.

Fetal health monitoring throughout pregnancy is challenging and complex. Complications in the fetal health not identified at the right time lead to mortality of the fetus as well the pregnant women. Hence, obstetricians check the fetal health state by monitoring the fetal heart rate (FHR). Cardiotocography (CTG) is a technique used by obstetricians to access the physical well-being of fetal during pregnancy. It provides information on the fetal heart rate and uterine respiration, which can assist in determining whether the fetus is normal or suspect or pathology. CTG data has typically been evaluated using machine learning (ML) algorithms in predicting the wellness of the fetal and speeding up the detection process.

In this work, we developed LightGBM with a Grid search-based hyperparameter tuning model to predict fetal health classification. The classification results are analysed quantitatively using the performance measures, namely, precision, Recall, F1-Score, and Accuracy Comparisons were made between different classification models like Logistic Regression, Decision Tree, Random Forest, k-nearest neighbors, Bagging, ADA boosting, XG boosting, and LightGBM, which were trained with the CTG Dataset obtained by the patented fetal monitoring system of 2,216 data points from pregnant women in their third trimester available in the Kaggle dataset. The dataset contains three classes: normal, suspect, and pathology. Our proposed model will give better results in predicting fetal health classification.

In this paper, the performance of the proposed algorithm LightGBM is compared and experimented with various Machine learning Techniques namely LR, DT, RF, KNN, Boosting, Ada boosting, and XG Boost and the classification accuracy of the respective algorithms are 84%, 94%, 93%, 88%, 94%, 89%, 96%. The LightGBM achieved a performance of 97% and outperforms the former models.

The LightGBM-based fetal health classification has been presented. Ensemble models were applied to the FHR dataset and presented the hybrid algorithm, namely Light GBM, and its application to fetal health classification. LightGBM has advantages that include fast training, improved performance, scale-up capabilities, and lesser memory usage than other ensemble models. The proposed model is more consistent and superior to other considered machine learning models and is suitable for the classification of fetal health based on FHR data. Finally, the outcomes of the multiple methods are compared using the same training and test data in order to verify the efficiency of LightGBM. The model can be further enhanced by making it hybrid by combining the advantages of different models and optimization techniques.

Cloud computing has become an essential technology for data storage and sharing, but security concerns remain significant challenges. Authentication is one of the critical components of cloud security. Various authentication schemes have been proposed to ensure the confidentiality and integrity of cloud data.

This research paper reviews the state-of-the-art three-factor authentication schemes based on smart cards, biometric authentication, and elliptic curve cryptography. The patent paper also presents a comprehensive overview of DNA cryptography algorithms, which have recently gained popularity due to their unique features in cloud data security. The paper further compares the DNA cryptography algorithms and discusses their advantages and disadvantages in terms of security and performance.

Finally, the patent paper proposes an efficient and secure DNA-based authentication scheme for cloud computing. This scheme is validated using all these types of data, and its performance is evaluated in terms of encryption and decryption times. Decryption takes 60 seconds on average and encryption 40 seconds on average.

As a final evaluation, it establishes that the proposed system can provide secure cloud services.