Recent Patents on Engineering - Current Issue

Recently, e-learning has become a very basic, integral part of technology-based learning. Wide trends are increasing day by day because of the demands and its usage based on working remotely due to highly penetrated mobile handheld devices and digital media. The smart campus infrastructure has played a vital role to its full extent towards Z millennium students in the 20^th century. The teaching and learning accessibility depends on terms of various cost-based affordable platforms, either with synchronous learning or asynchronous mode of learning.

The current patent research explores the changeling leading to infrastructural reforms as per the need for digital media for e-learning during and after COVID-19 spreads. The perspectives in 2 forms of research study are: 1^st working on infrastructural needs and demands for the smart campuses and online learning challenges and 2^nd is working on platforms technology utilization for better accessible resources for all learners. This work studied different aspects during and after COVID-19, leading to the importance of uninterrupted internet access, phone, hardware and reliability, etc. In this work, the importance of gamification study and flipped classrooms for enhancing learner performance to highly engage them in learning environments focused research model on learner engagement on Gamified perceiving study with Smart PLS-SEM was investigated. Promoting sustainability in its entirety through knowledge transfer and contributions to address various challenges in the redesign of learners' syllabi to meet educational needs, emphasizing online learning to integrate various modes of learner platforms, personalized teaching and learning, peer-to-peer communication for learner enhancement, and student engagement through gamification are studied.

Learners who are enrolled at the school, college, and university levels of education increased exponentially post-COVID-19. More than 90% responded to school closures with different learning abilities. Nearly 50% of countries in the world are merging guidance with faculty training. The enrolment in online courses has surged to more than 80%, and the success rates for online courses have increased to more than 70%. The eventual outcome is to emphasize the two aspects of the online platform of teaching and learning by giving students higher outcomes and intelligent aspects of a smart campus. The learners progress in terms of less network connectivity loss, efficient chat system, knowledge sharing, online assessments, micro-learning, increasing engagement, and gamification rewards in online learning. PLS-SEM results indicate the fitness values for a fit model with x²/df as 1.50 and RMSEA as 0.059.

From the learning prospective, the research focuses on inferring the importance of gamified learning applications for student learning satisfaction levels. This enhances and improves their fun learning competence.

The Cloud model is one of the most realistic frameworks with a vast range of social networking interactions. In medical data, security is a major constraint as it incorporates information about the patients. The cloud environment subjected to mobility and openness is exposed to security issues and limits authorization levels for data transmission.

This patent paper aims to propose a security model for attack prevention within the healthcare environment.

The proposed Cryptographic Attribute-based Machine Learning (CAML) scheme incorporates three stages. Initially, the homomorphic encryption escrow is performed for secure data transmission in the cloud. Secondly, the information of the users is evaluated based on the consideration of users' authorization. The authorization process for the users is carried out with the attribute-based ECC technique. Finally, the ML model with the classifier is applied for the detection and classification of attacks in the medical network.

The detected attack is computed and processed with the CNN model. Simulation analysis is performed for the proposed CAML with conventional ANN, CNN, and RNN models. The simulation analysis of proposed CAML achieves a higher accuracy of 0.96 while conventional SVM, RF, and DT achieve an accuracy of 0.82, 0.89 and 0.93, respectively.

With the analysis, it is concluded that the proposed CAML model achieves higher classification accuracy for attack detection and prevention in the cloud computing environment.

Today, Internet of Things applications offer new opportunities in all domains like home automation, transportation, medical diagnosis, agriculture, etc. According to McKinsey Global Institute research, IoT will cover a market share of over $11.1 trillion by 2025. Moreover, Semantic web technology approaches are used in IoT applications so that machines can understand and interpret sensor-collected data.

Our proposed system uses a DHT11 sensor, NodeMCU for data collection, and ThingSpeak cloud for data analysis and visualization. It utilizes the Protégé tool to develop semantic data modelling using Ontology/RDF graphs and retrieval for future SPARQL queries.

This patent approach ensures the optimal presentation of sensor data and the meaning of data and controls the information for the Home Automation System. By semantic layer, we improved integration, interoperability, discovery, and data analysis.

As far as applications are concerned, semantic technologies and IoT sensor data can be transformed into a more valuable and practical format, enabling intelligent applications and systems development across multiple fields, such as smart cities, industrial automation, healthcare, and environmental monitoring.

Due to their complexity and size, deploying ciphertexts for clouds is considered the most useful approach to accessing large data stores.

However, access to a user's access legitimacy and improving a decrypted text on the cloud depending on an improved access policy (AP) specified by the data owner are the key challenges for making large data storage realistic and effective in clouds. The traditional ways either totally remove the problem of AP development or offer renewal to arbiter power, but in real-time, enhancing the AP is essential to maximising security and handling agility.

In this patent paper, a safe and verifiable access control program characterised by the NTRU cryptographic system for large storage of data in the clouds is proposed. Primarily, an improved NTRU decryption protocol to deal with the decryption failures of the prime NTRU is established, and in addition, the program is analysed for its security strength and computational performance. When a new AP is specified by the data owner, the cloud server allows the program to improve ciphertext and allows the owner to verify the upgrade to oppose the cloud's fraudulent behaviour.

It enables (i) checking the user's legitimacy to access the data owner and qualified users, and (ii) allowing the user to verify the data provided by other users for the recovery of the right user. Strong analysis can prevent and block delinquency from various attacks, namely the collusion attack that could potentially target fraud users.

The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current state-of-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers.

The method for identifying cancer, together with machine learning and IoT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities.

This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data.

The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentation-improving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes.

The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area.

This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken.

Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art.

Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes.

The objective of this work is to analyze and predict the harmfulness in traffic accidents.

Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1].

In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability.

Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this patent work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety.

The increasing demand for electricity, coupled with an imbalanced supply and demand, population growth, and climate change, has prompted the shift from conventional to non-conventional energy systems. However, the unreliability and intermittency of the latter pose a challenge to their feasibility. To address this challenge, a proposal has been made to explore the combination of two renewable energy sources (RES) using a unique DC-DC converter topology, with the aim of meeting the load demand in a sustainable and efficient manner.

The focus of this research was to explore solutions for the challenges associated with operating RES independently, including issues with intermittency, weather dependence, and meeting load demands. The proposed hybrid system features exclusively RES, offering a promising approach to reducing carbon footprint. Ultimately, we aimed to develop a CUK-SEPIC-based converter that can effectively integrate two independent RES to satisfy the load demand of a standalone application.

Effective hybrid power generation through RES is a complex challenge, but it has been found that combining solar and biomass energy sources is one of the best options for achieving this goal. To tap into these sources, it is essential to have a suitable power electronic converter, and the CUK-SEPIC converter has been chosen for its many benefits. The features of this converter have been described in detail. The integration of solar and biomass energy sources is achieved using this converter, which has been designed and mathematically modeled in the MATLAB/Simulink environment to ensure optimal performance. To validate the effectiveness of the proposed converter, a comparison with existing power electronic converters has been done using the MATLAB/Simulink platform.

The hybrid power generation system model has been comprehensively developed in this work using the sophisticated MATLAB/Simulink environment. The input and output parameters have been diligently estimated through an extensive simulation process. The research has yielded valuable insights, indicating that the CUK-SEPIC converter exhibits an impressive power conversion efficiency of 96.57%, along with an overall step-up ratio of 5.25 and significantly reduced ripple content.

Upon conducting a comprehensive analysis of the CUK-SEPIC DC-DC converter, it has been observed that the proposed system exhibits significant promise in rectifying the reliability issues commonly associated with renewable energy power generation. Therefore, it is recommended that this system be considered for implementation in rural electrification initiatives. Furthermore, it is worth noting that this system represents one of the most recent developments in the field of renewable energy power generation technology and can be considered as one of the patents in engineering.

The prediction of the stock price is considered to be one of the most fascinating and important research and patent topics in the financial sector.

Making more accurate predictions is a difficult and significant task because the financial industry supports investors and the national economy.

The DWM is used to adjust the connection weights and biases to enhance prediction precision and convergence rate. DWM was proposed as a method to reduce system error by changing the weights of various levels. The methods for predictable changes in weight were provided together with the computational difficulty.

An extreme learning machine (ELM) is a fast-learning method for training a single-hidden layer neural network (SLFN). However, the model's learning process is ineffective or incomplete due to the randomly chosen weights and biases of the input's hidden layers. Hence, this article presents a deterministic weight modification (DWM) based ELM called DWM-ELM for predicting the stock price.

The calculated results showed that DWM-ELM had the best predictive performance, with RMSE (root mean square error) of 0.0096, MAE (mean absolute error) of 0.0563, 0.0428, MAPE (mean absolute percentage error) of 1.7045, and DS (Directional Symmetry) of 89.34.

The experimental results showed that, in comparison to other well-known prediction algorithms, the suggested DWM+ELM prediction model offers better prediction performance.

One of the main directions of modern technology in the field of precision machining is high-speed operation. The spindle system is commonly utilized in this kind of operation, and the electric spindle is the main preference among high-speed machine tool spindles.

High-speed electric spindle vibration characteristics affect the machining accuracy of the machine tool and the quality of the workpiece, so the research on high-speed electric spindle vibration characteristics has important engineering practical significance.

The research status of high-speed electrospindle at home and abroad has been summarized in this paper. Combined with the patents related to the dynamics modelling of electrospindle, the research on the dynamics modelling of high-speed electrospindle is analyzed. On this basis, the computational and analytical methods for the vibration modelling of the electrospindle, including the transfer matrix method and the finite element method, are investigated, the theoretical foundations of these methods are discussed in depth, and the advantages and disadvantages of the methods are evaluated. The applicability and limitations of the two methods are also compared.

The analysis has shown that the current research on the vibration characteristics of high-speed electrospindle is mainly based on mechanical modal analysis and electromagnetic analysis. At present, the dynamic modeling of the electrospindle mainly includes bearing modeling, shaft bearing modeling, spindle-case modeling, electrospindle electromechanical coupling modeling, electrospindle thermal coupling modeling, etc. The correctness of the modelling theory is verified through experimental and simulation results. Although these models tend to be perfected, they are still insufficient in the case of multiple influencing factors coupling and need further development.

Finally, through the analysis of the patent and dynamic characteristics related to the high-speed electric spindle, thermal deformation, magnetic tension, material, and other factors should be considered comprehensively, and these factors should be coupled to establish an overall dynamics model for the vibration characteristics analysis. The dynamic modelling, vibration modelling method, and vibration characteristics of the high-speed electric spindle have been summarized in this study, and the outlook is presented.

Epileptic Seizure (ES) is a neural disorder that generates an uncontrolled brain signal impulse. The disorder is seen in young children and adults with a positive medical history.

In this patent paper, a novel approach to epileptic seizure detection and prediction is proposed and evaluated. The seizure is retrained from Electroencephalography (EEG) high-dimension datasets. The EEG datasets further segment features of interdependent EEG into a matrix. This matrix is linked to providing a validation occurrence of similar feature events with a minimum redundancy maximum relevance (MRMR) approach for ES feature optimization.

The uncertainty-based genetic algorithm for parametric evaluation and validation (GAPEr) is used for predictive analysis and decision support via a dedicated neural networking model. The sizer detection and prediction are supported and validated via a series of interactions from trained datasets.

The proposed setup has achieved higher accuracy and dependency in decision support of Epileptic Seizure identification and classification based on predictive evaluation.

This work is completely new and done by the authors for the Assamese language. To build this tool, a few research papers on this language were studied, and a summary of them is written in the Related Work section.

In this experiment, 72 root verbs and 132 inflections were used. GUI was designed by using Tkinter in Python. The inflected verb form passed through three modules consisting of different searching techniques. Assamese corpus, Google search, Assamese Wikipedia, and Manual evaluation, respectively.

Finally, 662 Assamese valid verb forms were found that have meaning and are used in a different context. Corpus search, Google search, Assamese Wikipedia and Manual Evaluation gave 232, 733, 550 and 112 numbers of overall search results accordingly.

This paper presents a novel validation framework for Assamese language verb inflections and hence it can be defined as a patent work. This work can help the linguistic community better comprehend verb inflection patterns and linguistic diversity. This patent paper explores the inflections of a lesser-known low-resource language that would be beneficial. This study could be considered as innovative and important because it has applications in areas like speech recognition, machine translation, and natural language processing.

The linguistic community would benefit greatly from the creation of a new, freely accessible, user-friendly interface intended exclusively for validating Assamese verb inflections. The approaches used here to recognize correct inflections of Assamese verbs have been performed with in-depth analysis. The approach could have implications for language learning and education, improving machine translation and natural language processing systems. Validating the verb inflections may help in raising the awareness and recognition of research focused on underdeveloped or marginalized languages. As a result, speakers of these languages might have more equitable access to information, resources, and opportunities, thus resulting in a decrease in linguistic prejudice.

The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables real-time decision-making in communication, collaboration, and technology analytics. Furthermore, environmental factors impacting plants, such as air humidity, temperature, air quality index, and soil wetness, are not frequently documented, emphasizing the development of a data monitoring system for future agricultural research and development.

An IoT-based Intelligent Farming System is proposed. An innovative IoT-based intelligent farming system is developed that integrates real-time data monitoring, machine learning algorithms, and IoT technology to address the identified gaps and challenges.

In the face of climate change, extreme weather, and environmental constraints, increased food demand must be satisfied. Intelligent agriculture enabled by IoT technology can reduce waste and increase productivity for producers and farmers, from fertilizer use to tractor trips.

In conclusion, this patent paper provides insightful and informative commentary on the progress made in technology within the agriculture industry and the challenges that still need to be overcome to achieve optimal outcomes.