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Enhancing Disease Prediction in Healthcare: A Comparative Analysis of PSO and Extreme Learning Approach
The healthcare business generates a tremendous quantity of data, and the goal is to collect it and use it effectively for analysis, prediction, and treatment. The best approach to disease management is disease prevention through early intervention. There are a number of methods that can advise you on how to treat a specific sickness, but much fewer that can tell you with any degree of certainty if you will actually get sick in the first place. Preprocessing, feature selection, feature extraction, and model training are all parts of the proposed method. The suggested layout includes a preprocessing stage that takes care of things like moving average, missing values, and normalization. Feature selection describes the process of selecting the most relevant features from a dataset. After gathering features, the models are trained using PSO-ELM. The proposed strategy is superior to the widely used PSO and ELM. 2023 IEEE. -
Enhancing Early Detection of Cardiovascular Disease through Feature Optimization Methods
cardiovascular diseases are the most common reason for mortality around the world. Early detection of the ailment can help to reduce the mortality rate considerably. The ever-growing technologies like machine learning algorithms and deep learning models can be used for this purpose. The AI models thus developed can be used for health sector for assisting doctors in assessing the stage of the disease and detection and tracking of the clots in the cardio blood vessels. The proposed work uses two benchmark datasets for analysing the performance of various machine learning algorithms including KNN, Nae Bayes, Decision Tree and Random Forest. The performance was compares based on the AUC %. The method feature reduction were used here to reduce the computational complexity of the model. The results show that Random Forest Algorithm gave the best result when compared to other algorithms in case of UCI dataset and MLP classifier gave best results for Kaggle dataset. 2024 IEEE. -
Enhancing Educational Adaptability: A Review and Analysis of AI-Driven Adaptive Learning Platforms
This study explores the transformative potential of AI-powered adaptive learning platforms (ALPs) in education, specifically focusing on personalized learning paths and their impact on student engagement and outcomes. Through a comprehensive analysis of four prominent ALPs - Carnegie Learning, DreamBox Learning, Smart Sparrow, and Knewton - this study investigates their approaches to content tailoring and feedback delivery. The comparative analysis highlights each platform's strengths and limitations, providing educators with valuable insights for informed selection and implementation. This study also considers the broader landscape of ALPs, acknowledging concerns such as bias, data privacy, and the role of educators in the tech-driven educational environment. The findings contribute to our understanding of how ALPs can empower educators, personalize learning, and address achievement gaps, offering a nuanced perspective on the complex tapestry of AI in education. 2024 IEEE. -
ENHANCING FAKE NEWS DETECTION ON SOCIAL MEDIA THROUGH ADVANCED MACHINE LEARNING AND USER PROFILE ANALYSIS
Social media news consumption is growing in popularity. Users find social media appealing because it's inexpensive, easy to use, and information spreads quickly. Social media does, however, also contribute to the spread of false information. The detection of fake news has gained more attention due to the negative effects it has on society. However, since fake news is created to seem like real news, the detection performance when relying solely on news contents is typically unsatisfactory. Therefore, a thorough understanding of the connection between fake news and social media user profiles is required. In order to detect fake news, this research paper investigates the use of machine learning techniques, covering important topics like feature integration, user profiles, and dataset analysis. To generate extensive feature sets, the study integrates User Profile Features (UPF), Linguistic Inquiry and Word Count (LIWC) features, and Rhetorical Structure Theory (RST) features. Principal Component Analysis (PCA) is used to reduce dimensionality and lessen the difficulties presented by high-dimensional datasets. The study entails a comprehensive assessment of multiple machine learning models using datasets from "Politifact" and "Gossipofact," which cover a range of data processing methods. The evaluation of the XGBoost classification model is further enhanced by the analysis of Receiver Operating Characteristic (ROC) curves. The results demonstrate the effectiveness of particular combinations of features and models, with XGBoost outperforming other models on the suggested unified feature set (ALL). 2023 Little Lion Scientific. -
Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing
Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results. 2024 The Author(s) -
ENHANCING FOREST ECOSYSTEM RESILIENCE TO CLIMATE CHANGE WITH VANET AND INTEGRATED NATURAL RESOURCES MODELLING
Forest ecosystems are immediately threatened by rising global temperatures and changing climatic patterns. Periodic assessments also contribute to a reduction in the frequency of monitor-ing, which could cause environmental changes to go unnoticed. This work develops a novel real-time monitoring and early warning system to meet this difficulty. By integrating Vehicular Ad Hoc Networks (VANET) with sophisticated natural resources modelling, the proposed method aims to revolutionise the way forest ecosystems are managed. This study strives to design and implement a comprehensive system that harnesses the power of VANET to collect real-time data from sensors deployed on vehicles, and integrates advanced modelling to predict, assess, and mitigate risks to forest ecosystems. The proposed method involves deploying a network of vehicles equipped with environmental sensors within VANET. These sensors continuously collect data on crucial environmental parameters, such as temperature, humidity, air quality, and spatial information. The data are transmitted through a secure VANET communication protocol to a centralised processing unit, where it is integrated with climate models and ecosystem dynamics models. Resilience metrics and thresholds are defined to trigger a tiered early warning system. Preliminary testing of the system demonstrates promising accuracy and responsiveness. The integrated approach allows for dynamic risk assessment, enabling the identification of potential threats such as extreme weather events, invasive species, or disease outbreaks. Early warnings prompt adaptive management strategies, showcasing the systems potential to significantly enhance forest ecosystem resilience. This research presents a pioneering solution to the escalating challenges faced by forest ecosystems in the time of climate change. The real-time monitoring, early warning system, amalgamating VANET and integrated modelling, stand as a robust tool for forest managers, policymakers, and communities to proactively address environmental changes. The findings underscore the systems potential to transform forest management practices, marking a critical step toward sustainable and resilient ecosystems. 2024, Scibulcom Ltd. All rights reserved. -
Enhancing Greedy Web Proxy caching using Weighted Random Indexing based Data Mining Classifier
Web Proxy caching system is an intermediary between the Web users and servers that try to alleviate the loads on the origin servers by caching particular Web objects and behaves as the proxy for the server and services the requests that are made to the servers. In this paper, the performance of a Proxy system is measured by the number of hits at the Proxy. Higher number of hits at the Proxy server reflects the effectiveness of the Proxy system. The number of hits is determined by the replacement policies chosen by the Proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The performance of the web proxy caching system is improved by adapting Data Mining Classifier model based on Web User clustering and Weighted Random Indexing Methods. The outcome of the paper are proactive strategies that augment the traditional replacement policies such as GDS, GDSF, GD? which uses the Data Mining techniques. 2019 -
Enhancing greedy web proxy caching using weighted random indexing based data mining classifier /
Egyptian Informatics Journal, Vol.20, Issue 2, pp. 117-130, ISSN No. 1110-8665. -
ENHANCING home security through visual CRYPTOGRAPHY
Home security systems in the recent times have gained greater importance due to increasing threat in the society. Biometrics deals with automated approaches of recognizing a user or verifying the user identity based on behavioral or physiological features. Visual cryptography is a scheme of secret sharing where a secret image is encrypted into shares which disclose no data independently about the original secret image. As the template of biometric are stored in centralized database due to the threats of security the template of biometric may be changed by attacker. If the template of biometric is changed then the authorized user will not be permitted to access the resource. To manage this problem the schemes of visual cryptography can be used to secure the face recognition. Visual cryptography offers huge ways for supporting such needs of security as well as additional authentication layer. To manage this problem the visual cryptography schemes can be used to secure digital biometric information privacy. In this approach the face or private image is dithered in two varied host images that is sheets and are stored in separate servers of data so as to assure that the original image can get extracted only by accessing both sheets together at a time and a single sheet will not be capable to show any data of private image. The main aim of the study is to propose an algorithm which is a combination of CVC and Siamese network. This research implements visual cryptography for face images in a biometric application. The Siamese network is essential to solve one shot learning by representation of learning feature that are compared to verification tasks. In this research face authentication helps in accomplishing robustness by locating face image from an n input image. This research explores the availability of using visual cryptography for securing the privacy to biometric data. The results of the proposed approach provide an accuracy of 93% which is found to be superior when compared with that of the approaches that are already in practice. 2020 -
Enhancing Human-Computer Interaction with a Low-Cost Air Mouse and Sign Language Recognition System
The purpose of this study is to investigate the development of assistive technologies that are designed to empower people with disabilities by increasing their level of freedom and accessibility. Voice assistants, air mice, and software that recognizes sign language are some of the topics that are specifically covered in this. Those who have impaired fine motor skills can benefit from using air mice since they allow controls to be made by hand gestures. Using machine learning algorithms, sign language recognition software is able to decipher signs with an accuracy rate of over 90 percent, making it easier for people who are deaf or hard of hearing to communicate themselves. By relying solely on vocal instructions, voice assistants like Alexa make it possible to control devices without using your hands. Not only do these technologies have the potential to be revolutionary, but they also confront obstacles in terms of improving identification accuracy and integrating them into common gadgets. In this study, the development and impact of voice assistants, sign language software, and air mice are discussed. More specifically, the paper highlights the potential for these technologies to help millions of people with disabilities all over the world. Additionally, it examines potential enhancements that could be made to these technologies in the future in order to further improve accessibility and inclusivity. This research integrates computer vision and machine learning to create a multimodal system blending air mouse functionality with real-time sign language translation. Achieving 95% accuracy in gesture recognition for air mouse control and 98% accuracy in sign language letter classification using a basic webcam, the system promotes accessible interaction without specialized hardware. Despite limitations in vocabulary and lighting sensitivity, future efforts aim to broaden data training and explore mobile deployment. These advancements hold promise for enhancing natural human-computer interaction, particularly for users with disabilities, by enabling intuitive, hands-free control and communication. 2024 IEEE. -
Enhancing Industrial Equipment Reliability: Advanced Predictive Maintenance Strategies Using Data Analytics and Machine Learning
In today's dynamic industrial landscape, optimizing machinery performance and minimizing downtime are paramount for sustained operational excellence. This paper presents advanced predictive maintenance strategies, with a focus on leveraging machine learning and data analytics to enhance the reliability and efficiency of industrial equipment. The study explores the key components of predictive maintenance, including data collection, condition monitoring, predictive models, failure prediction, optimized maintenance scheduling and the extension of equipment longevity. The paper discusses how predictive maintenance aligns with modern industrial paradigms. The study evaluated the performance of five popular forecasting models like Random Forest, Linear Regression, Exponential Smoothing, ARIMA, and LSTM, to estimate maintenance for industrial equipment. The effectiveness of each model was evaluated using a number of performance metrics. The percentage of the variation in the real data that the model can explain is shown by the R-squared number. The lowest MSE, RMSE, and greatest R-squared values indicate a model's accuracy. The study highlights practical implications across diverse industries, showcasing the transformative impact of predictive maintenance on minimizing unplanned downtime, reducing maintenance costs, and maximizing the lifespan of critical machinery. When it comes to predictive maintenance for industrial machinery, the LSTM model has been shown to be the most accurate and efficient model with the highest R-squared value, indicating a better fit and higher predictive ability. As technology continues to evolve, the paper discusses future directions, including the integration of artificial intelligence and advanced analytics, and emphasizes the importance of continuous improvement in refining predictive maintenance strategies for the evolving needs of industries worldwide. 2024 IEEE. -
Enhancing instructional effectiveness using the metaverse: An empirical analysis of the role of attitude and experience of participants
The Metaverse has been gaining importance, with businesses looking to adopt the same for processes rangingfrom onboarding to customer experience. The current study has been conducted to evaluate the impact of learner characteristics on motivation to participate in metaverse-based training programs across various organizations. Based on literature and theory, two main characteristics were identified: attitude towards the metaverse and experience with the technology. Data for the study was collected using a structured questionnaire and 103 responses were collected from employees belonging to various organizations in India. The analysis and interpretation of the data was done using statistical techniques through the tool of SPSS. The study found out that both the learner characteristics have a strong positive relationship with each other, and attitude towards metaverse has a stronger relationship with learner motivation than the experience of use. The findings suggest organizations focus more on the manner in which they should introduce metaverse at the workplaces and need to keep the employee attitude towards any kind of change; more of a technological change in mind when they are strategizing to implement metaverse-based training programs. 2024, IGI Global. All rights reserved. -
Enhancing IoT Security Through Deep Learning-Based Intrusion Detection
The Internet of Things (IoT) has revolutionized the way we interact with technology by connecting everyday devices to the internet. However, this increased connectivity also poses new security challenges, as IoT devices are often vulnerable to intrusion and malicious attacks. In this paper, we propose a deep learning-based intrusion detection system for enhancing IoT security. The proposed work has been experimented on IoT-23 dataset taken from Zenodo. The proposed work has been tested with 10 machine learning classifiers and two deep learning models without feature selection and with feature selection. From the results it can be inferred that the proposed work performs well with feature selection and in deep learning model named as Gated Recurrent Units (GRU) and the GRU is tested with various optimizers namely Follow-the-Regularized-Leader (Ftrl), Adaptive Delta (Adadelta), Adaptive Gradient Algorithm (Adagrad), Root Mean Squared Propagation (RmsProp), Stochastic Gradient Descent (SGD), Nesterov-Accelerated Adaptive Moment Estimation (Nadam), Adaptive Moment Estimation (Adam). Each evaluation is done with the consideration of highest performance metric with low running time. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Enhancing IoT Security Through Multilayer Unsupervised Learning and Hybrid Models
This research addresses the challenge of limited unsupervised learning in current IoT security research, which heavily relies on labelled datasets, hindering the detection of unknown threats. To overcome this constraint, the study proposes a sophisticated methodology integrating K-means clustering, autoencoders, and a hybrid model (combining both). The aim is to enhance detection capabilities without being reliant on prior labelled data. Emphasizing the need to go beyond traditional models, the research underscores the significance of incorporating a diverse range of smart home IoT devices to gain comprehensive insights. Tests conducted on the N-BaIoT dataset, which incorporates authentic traffic data from nine commercial IoT devices afflicted with Mirai and BASH-LITE infections, demonstrate the effectiveness of the suggested models. K-means clustering demonstrates excellence in precision, recall, and F1-scores, particularly in Doorbell and Thermostat categories. The Hybrid model consistently achieves high precision and recall metrics across various device categories by leveraging the strengths of both Kmeans and autoencoder techniques. Notably, the Autoencoder model stands out for its exceptional ability to achieve a perfect 100% detection rate for anomalies across all devices. This study highlights the robust performance of the proposed unsupervised learning models, emphasizing their strengths and potential areas for refinement in enhancing IoT network security. 2024 IEEE. -
Enhancing Kubernetes Auto-Scaling: Leveraging Metrics for Improved Workload Performance
Kubernetes is an open-source production-grade container orchestration platform, that can enable high availability and scalability for various types of workloads. Maximizing the performance and reducing the cost are two major challenges modern applications encounter. To achieve this, resource management and proactively deploying resources to meet specific application requirements becomes utmost important. Adopting Kubernetes auto-scaler to fit one's needs are important to maximize the performance. This study aims to perform a comprehensive analysis of Kubernetes auto-scaling policies. This paper also lists out the various parameters considered for auto-scaling, and prediction methods used to efficiently handle resource requirements of applications. The research findings reveal a scarcity in the existing work regarding the variety of workload based auto-scaling and custom metrics. This paper provides a concise overview of a forthcoming research endeavor that explores the utilization of custom metrics in conjunction with auto-scaling. 2023 IEEE. -
Enhancing language teaching materials through artificial intelligence: Opportunities and challenges
Incorporating artificial intelligence (AI) into language education signifies a paradigm shift that promotes originality and inclusiveness. The partnership between AI developers and educators effectively tackles obstacles and establishes a foundation for continuous progress. Anticipating the future, the progression of AI holds the potential to deliver intricate customization, customizing educational encounters to suit the unique requirements of each individual. Responsible incorporation of AI into teaching methodologies transforms them into a collaborative model that empowers educators to engage in individualized interactions. Ethics remain of the utmost importance, encompassing bias mitigation and privacy. In essence, the integration of AI into language education signifies an impending era in which the combined powers of technology and human proficiency foster the development of capable individuals who are prepared to navigate an interconnected, digitally globalized society. 2024, IGI Global. All rights reserved. -
Enhancing Medical Decision Support Systems withtheTwo-Parameter Logistic Regression Model
The logistic regression model is an invaluable tool for predicting binary response variables, yet it faces a significant challenge in scenarios where explanatory variables exhibit multicollinearity. Multicollinearity hinders the models ability to provide accurate and reliable predictions. To address this critical issue, this study introduces innovative combinations of Ridge and Liu estimators tailored for the two-parameter logistic regression model. To evaluate the effectiveness of the combination of ridge and Liu estimators under the two-parameter logistic regression, a real-world dataset from the medical domain is utilized, and Mean Squared Errors are employed as a performance metric. The findings of our investigation revealed that the ridge estimator, denoted as k4, outperforms other Liu estimators when multicollinearity is present in the data. The significance of this research lies in its potential to enhance the reliability of predictions for binary outcome variables in the medical domain. These novel estimators offer a promising solution to the multicollinearity challenge, contributing to more accurate and trustworthy results, ultimately benefiting medical practitioners and researchers alike. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Enhancing mobility management in 5G networks using deep residual LSTM model
Mobility management is an essential component of 5G networks to provide mobile users with seamless connectivity and efficient cell transition. However, increasing user mobility, device density, and the diversity of service requirements all pose significant challenges to achieving optimal mobility management. This article describes a novel method for improving mobility management in 5G networks that employs a deep residual Long Short-Term Memory model. Deep learning and LSTM, a type of recurrent neural network, are used in the proposed model to identify temporal dependencies and patterns in user mobility data. The model learns to predict future user locations and mobility patterns by training on historical mobility data, allowing for proactive resource allocation and handover decisions. We incorporate residual connections into the LSTM architecture, inspired by the residual learning framework, to address the inability of traditional LSTM models to capture complex temporal dynamics. This allows the model to effectively incorporate long-term dependencies and improves prediction accuracy. Furthermore, we incorporate the mLSTM model into the mobility management framework of 5G networks. The model continuously obtains real-time user location updates and predicts future user positions, allowing for proactive handover decisions. The network can optimize resource allocation, reduce handover latency, and improve user experience by leveraging anticipated mobility patterns. We test the proposed method by simulating it extensively with real-world mobility traces. The results show that the mLSTM model accurately predicts user mobility and outperforms conventional methods in transition performance. The model is not affected by changing network conditions, user mobility patterns, or service specifications. 2024 Elsevier B.V. -
Enhancing Mobility: A Smart Cane with Integrated Navigation System and Voice-Assisted Guidance for the Visually Impaired
Blindness is a condition which affects many people, and for the affected people, quality of life can take a big hit. Most blind people already use walking sticks to feel the terrain in front of them as they move around and navigate using touch and sound. However, they cannot judge distances to objects until the cane actually hits the object. In some cases, the contact with the cane may damage the object. Hence, it may be better to have some early warning system so that there is less likelihood of causing damage. This paper presents the design and development of a 'Smart Cane' aimed at enhancing mobility and safety for visually impaired individuals. The cane incorporates ultrasonic sensors to detect objects in the user's surroundings. When an object is detected within a specified distance range, the cane provides haptic feedback through a bidirectional vibration motor, alerting the user to its presence. The microcontroller-based system processes data from both sensors and efficiently manages power consumption to ensure extended battery life. The device's design includes user-friendly controls and an ergonomic enclosure to offer ease of use and protection for the electronic components. Further, there is built-in navigation via online Map API. With the convenience of navigating oneself without external assistance, the 'Smart Cane' demonstrates great potential to improve the independence and confidence of visually impaired individuals in navigating their environments safely. 2024 IEEE. -
Enhancing Movie Genre Classification through Emotional Intensity Detection: An Improvised Machine Learning Approach
Movie Genre Classification through Emotion Intensity is a computer vision technique used to identify facial emotion through a sequential neural network model and to get the genre of the movie with it. This paper delves into latest advancements in Emotion Detection, particularly emphasizing neural network models and leveraging face image analysis algorithms for emotion recognition. Grenze Scientific Society, 2024.