Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health. This paper proposed a technique to predict brain strokes with high accuracy. The model was constructed using data related to brain strokes. The aim of this work is to use Multi Layer Perceptron (MLP) as a classification technique for stroke data and used multi-optimizers that include Adaptive moment estimation with Maximum (AdaMax), Root Mean Squared Propagation (RMSProp) and Adaptive learning rate method (Adadelta). The experiment shows RMSProp optimizer is best with a data training accuracy of 95.8% and a value for data testing accuracy of 94.9%. The novelty of work is to incorporate multiple optimizers alongside the MLP classifier which offers a comprehensive approach to stroke prediction, providing a more robust and accurate solution. The obtained results underscore the effectiveness of the proposed methodology in enhancing the accuracy of brain stroke detection, thereby paving the way for potential advancements in medical diagnosis and treatment

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models

A Multiple Criteria Decision Analysis based Approach to Remove Uncertainty in SMP Models

Advanced AI technologies are serving humankind in a number of ways, from healthcare to manufacturing. Advanced automated machines are quite expensive, but the end output is supposed to be of the highest possible quality. Depending on the agility of requirements, these automation technologies can change dramatically. The likelihood of making changes to automation software is extremely high, so it must be updated regularly. If maintainability is not taken into account, it will have an impact on the entire system and increase maintenance costs. Many companies use different programming paradigms in developing advanced automated machines based on client requirements. Therefore, it is essential to estimate the maintainability of heterogeneous software. As a result of the lack of widespread consensus on software maintainability prediction (SPM) methodologies, individuals and businesses are left perplexed when it comes to determining the appropriate model for estimating the maintainability of software, which serves as the inspiration for this research. A structured methodology was designed, and the datasets were preprocessed and maintainability index (MI) range was also found for all the datasets expect for UIMS and QUES, the metric CHANGE is used for UIMS and QUES. To remove the uncertainty among the aforementioned techniques, a popular multiple criteria decision-making model, namely the technique for order preference by similarity to ideal solution (TOPSIS), is used in this work. TOPSIS revealed that GARF outperforms the other considered techniques in predicting the maintainability of heterogeneous automated software.Comment: Submitted for peer revie

The Role of the Internet of Things in Health Care: A Systematic and Comprehensive Study

The Internet of Things (IoT) is becoming an emerging trend and has significant potential to replace other technologies, where researchers consider it as the future of the internet. It has given tremendous support and become the building blocks in the development of important cyber-physical systems and it is being severed in a variety of application domains, including healthcare. A methodological evolution of the Internet of Things, enabled it to extend to the physical world beyond the electronic world by connecting miscellaneous devices through the internet, thus making everything is connected. In recent years it has gained higher attention for its potential to alleviate the strain on the healthcare sector caused by the rising and aging population along with the increase in chronic diseases and global pandemics. This paper surveys about various usages of IoT healthcare technologies and reviews the state of the art services and applications, recent trends in IoT based healthcare solutions, and various challenges posed including security and privacy issues, which researchers, service providers and end users need to pay higher attention. Further, this paper discusses how innovative IoT enabled technologies like cloud computing, fog computing, blockchain, and big data can be used to leverage modern healthcare facilities and mitigate the burden on healthcare resources

Authorship identification using ensemble learning

With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing. Researchers are searching for alternative strategies to identify the author of an unknown text. There is a need to develop a system to identify the actual author of unknown texts based on a given set of writing samples. This study presents a novel approach based on ensemble learning, DistilBERT, and conventional machine learning techniques for authorship identification. The proposed approach extracts the valuable characteristics of the author using a count vectorizer and bi-gram Term frequency-inverse document frequency (TF-IDF). An extensive and detailed dataset, All the news is used in this study for experimentation. The dataset is divided into three subsets (article1, article2, and article3). We limit the scope of the dataset and selected ten authors in the first scope and 20 authors in the second scope for experimentation. The experimental results of proposed ensemble learning and DistilBERT provide better performance for all the three subsets of the All the news dataset. In the first scope, the experimental results prove that the proposed ensemble learning approach from 10 authors provides a better accuracy gain of 3.14% and from DistilBERT 2.44% from the article1 dataset. Similarly, in the second scope from 20 authors, the proposed ensemble learning approach provides a better accuracy gain of 5.25% and from DistilBERT 7.17% from the article1 dataset, which is better than previous state-of-the-art studies

A Secure Architectural Model using Blockchain and Estimated Trust Mechanism in Electronic Consumers

Consumer electronics devices, such as refrigerators, washing machines, TVs, smartphones, and household appliances, have become integral to human activities. However, these devices are vulnerable to security breaches and cyber-criminal threats, which can result in the theft and misuse of sensitive information. Existing security surveys and proposed schemes have encountered limitations in terms of redundancy and effectiveness. In this paper, we present a novel approach that ensures secure and transparent communication in consumer electronics. We introduce a multi-criterion decision-making model called TOPSIS, along with a weighted product model, to enhance the security and accuracy of the system. Furthermore, our proposed scheme employs a blockchain system for continuous tracking and monitoring of devices, ensuring accountability and surveillance of their past communications. Through comprehensive validation and verification against existing approaches using various security metrics, our proposed scheme demonstrates superior performance and effectiveness