Browse Items (11855 total)
Sort by:
-
Enhancing Natural Gas Price Prediction: A Machine Learning and Explainable AI Approach
This research includes an innovative approach to refine natural gas price predictions by employing advanced machine learning techniques, including Random Forest, Linear Regression, and Support Vector Machine algorithms. Against the backdrop of natural gas's increasing influence in the energy sector, both environmentally and economically, the study adopts a robust methodology using a comprehensive dataset from Kaggle. Through rigorous data preprocessing, feature engineering, and model training, the chosen algorithms are optimized to capture complex patterns within the data, demonstrating the potential to significantly enhance forecast precision. The application of these techniques aims to extract meaningful insights, providing stakeholders in the natural gas market with more accurate and reliable predictions, there by contributing to a deeper understanding of market dynamics and informed decision- making. 2024 IEEE. -
Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection
With the ever-increasing network systems and dependency on digital technologies, ensuring the security and integrity of these systems is of paramount importance. Intrusion detection systems (IDS) play a major role in sheltering such systems. Intrusion detection systems are technologies that are designed to monitor network and system activities and detect suspicious, unauthorized, malicious behavior. This research paper conducts a comprehensive comparative analysis of three popular machine learning algorithmsK-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR)in the context of intrusion detection using the renowned NSL-KDD dataset. Preprocessing techniques are applied, and the dataset is split for rigorous evaluation. The findings of this research highlight the effectiveness of Random Forest in detecting intrusions, showcasing its potential for real-world network security applications. This study contributes to the field of intrusion detection and offers valuable insights for network administrators and cybersecurity professionals to enhance network protection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Enhancing neurocognitive skills for effective leadership and decision-making
In today's dynamic workplace, human resource development and management (HRDM) professionals face multifaceted challenges requiring advanced cognitive abilities. This book chapter explores the critical interplay between leadership skills, decision-making, and executive functions (EFs) in HRDM. It sheds light on their pivotal role in shaping workplace dynamics and organizational outcomes. Focusing on skills such as emotional intelligence, cognitive flexibility, and continuous learning, the chapter delves into their neurocognitive underpinnings, particularly within the prefrontal cortex. It discusses strategies for enhancing EFs, including reflective practice, empathy training, and mindfulness, and emphasizes the concept of neuroplasticity in fostering continuous learning and adaptation within HRDM. By integrating insights from neuroscience into HR practices, the chapter offers valuable guidance for HR professionals seeking to optimize organizational performance, enhance leadership qualities, and drive effective decision-making processes. 2024 by IGI Global. All rights reserved. -
Enhancing Patient Safety and Efficiency in Intravenous Therapy: A Comprehensive Analysis of Smart Infusion Monitoring Systems
Intravenous (IV) fluids, comprising vitamin-rich solutions, are administered to address patient electrolyte imbalances and dehydration through IV infusion therapy. Infusion pumps are integral for precise medication dosage delivery in this common medical procedure, generally posing low risks. These fluids are stored in polypropylene bags connected to patients through tubes. However, when the IV bag empties, the patients blood may flow backward into the IV tube due to higher blood pressure, known as diffusion, potentially leading to complications like air embolism-life-threatening if air enters the bloodstream through the IV line, obstructing blood flow to vital organs. Smart IV Bags emerged as a solution to mitigate such risks, eliminating the need for manual IV bag monitoring while preventing reverse blood flow. This research comprehensively assesses various IoT-enabled IV Bag monitoring systems, comparing their strengths, weaknesses, and unique features. Key evaluation criteria include component efficiency, real-world applicability, accuracy, latency, and technical specifications. The aim is to provide an objective evaluation of each Smart Intravenous Liquid Monitoring System to inform future developments in this field. A systematic approach ensures the selection of systems that best meet specific requirements in diverse healthcare environments. 2024 Scrivener Publishing LLC. -
Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network
This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. 2024 IEEE. -
Enhancing performance of WSN by utilising secure QoS-based explicit routing
Wireless sensor networks (WSN) are infrastructure less and self-configured a wireless network that allows monitoring the physical conditions of an environment. Many researchers focus on enhancing the performance of WSN in order to provide effective delivery of data on the network, but still results in lower quality of services like energy consumption, delay and routing. We tackle this problem by introducing a new routing algorithm, QoS-based explicit routing algorithm which helps in transmitting the data from source node to destination node on WSN. We also involve clustering process in WSN based on genetic algorithm and particle swarm optimisation (GA and PSO) algorithm. We proposed identity-based digital signature (IBDS) and enhanced identity-based digital signature (EIBDS) that involves reduction of computation overhead and also increasing resilience on the WSN. We also use advanced encryption standard (AES), for ensuring the security between nodes and avoid hacking of data by other intruders. Copyright 2020 Inderscience Enterprises Ltd. -
Enhancing photocatalytic performance through surfactant-assisted electrochemical synthesis: Surface modification of hierarchical ZnO morphologies with Ag/ZnWO4 nanoparticles
This study presents the synthesis of surface-decorated CTAB-capped ZnO nanoparticles doped with Ag/ZnWO4 through a surfactant-assisted electrochemical synthesis approach. The development of surface-decorated composites is of considerable interest for enhancing photocatalytic efficiency. We report the synthesis of pristine, binary, and surface-decorated ZnO catalysts, specifically Zn, Zn/Ag, Zn/ZnWO4, and Zn/Ag/ZnWO4. Various methods for physicochemical characterization have been utilized to verify the catalysts' structural, optical, and morphological properties. The results demonstrate the successful surfactant capping and metal doping. The synthesized nanoparticles have been tested for their photocatalytic performance against Malachite Green, an environmentally harmful organic dye, across various reaction conditions. Scavenger studies reveal that the photodegradation process is primarily driven by superoxide and hydroxyl radicals and, to a lesser extent, by photogenerated holes. The decrease in electron-hole pair recombination in the Zn/Ag/ZnWO4 photocatalyst results in an enhanced degradation of Malachite Green when exposed to visible light. 2024 Elsevier B.V. -
Enhancing power conversion efficiency in five-level multilevel inverters using reduced switch topology
This paper presents extensive research on improving the power conversion efficiency of five-level multilevel inverters (MLIs) by utilizing a reduced switch topology. MLIs have received an abundance of focus because of their ability to generate high-quality output waveforms and have better harmonic outcomes than traditional two-level inverters. The high number of switches in MLIs, on the other hand, can result in increased power losses and lower overall efficiency. In this paper, a novel reduced switch topology for five-level MLIs, which is having five switches is proposed with the aim of minimizing power losses while preserving superior performance due to lesser number of switches. To achieve efficient power conversion, the proposed topology employs advanced pulse width modulation control strategies and optimized switching patterns. The simulation results show that the minimized switch topology improves the power conversion efficiency of the five-level MLI, resulting in lower losses and better overall system performance. The total harmonic distortion (THD) value of the output current has been reduced to 7.12% and the efficiency has been achieved to 96.92%. The findings of this investigation help to advance MLI technology, allowing for more efficient and reliable power conversion in a variety of applications such as renewable energy systems, electric vehicles, and industrial drives. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm
Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications. 2024 Elsevier B.V. -
Enhancing red wine quality prediction through Machine Learning approaches with Hyperparameters optimization technique
In light of the intricacy of the winemaking process and the wide variety of elements that could affect the taste and quality of the finished product, predicting red wine quality is difficult. ML methods have been widely used in forecasting red wine quality from its chemical characteristics in recent years. This Paper evaluated the comparison of classification and regression methods to predict the quality of red wine and performed the initial data analysis and exploratory data analysis on the data. This study implemented different Classifiers and Regressors that were trained and tested. Contrasted and Comparative analysis of the accuracies of eight models with hyperparameter tuning optimization, including Logistic Regression, Gradient Boosting, Extra Tree, Ada Boost, Random Forest, Support Vector Classifier, and Decision Tree, Knn and measured the classification report with F1, Accuracy, and Recall Scores. For Imbalance data, SMOTE Classifier was used. This study performed the Cross-validation technique, such as Grid search and with the best hyperparameters tuning. The study's findings demonstrated that the Gradient Boosting technique accurately predicted red wine quality. This research shows the promising results of Gradient Boosting for predicting red wine quality and adds important context to the usage of machine learning classifiers for this challenge. 2023 IEEE. -
Enhancing Retailer Auctions and Analyzing the Impact of Coupon Offers on Customer Engagement and Sales Through Machine Learning
Systems that use coupons have been used extensively to boost customer interaction on platforms having a digital component. We use causal machine learning techniques to determine the effect of an advertising intervention, especially a discount offer, on the bids of a shop. Discount shopping coupons are a popular tactic for increasing sales. The largest challenge for dealers is accurately anticipating the wants of their customers, and here is where they always struggle. Machine learning algorithms have been utilized by researchers to address a variety of problems. Selecting the right coupon is a challenging undertaking because every customer's behavior differs depending on the deal. Due to categorical data adjustments being necessary due to the majority of characteristics having missing values, the situation is made more difficult. The dataset is used to classify the dataset, and machine learning algorithms like logistic regression, random forest and SVM model, decision tree and naive bayes models are used to determine the correctness of the classification. 2023 IEEE. -
Enhancing Security and Resource Optimization in IoT Applications with Blockchain Inclusion
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data-driven applications. However, optimizing the allocation of resources within IoT networks is a pressing challenge. This research explores a novel approach to resource optimization, combining blockchain technology with enhanced security measures, while addressing the critical concerns of time and energy consumption. In this study, we propose a resource allocation framework that leverages the transparency and immutability of blockchain to enhance data integrity and security in IoT applications. The blockchain-based method is utilized to identify the malicious users in the IoT applications. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. To further optimize this process, we introduce a Hybrid Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), a powerful optimization technique designed to minimize time delays and energy consumption in IoT environments. Our findings demonstrate the effectiveness of the proposed approach in achieving resource efficiency, reducing time and conserving energy within IoT networks. 2023 IEEE. -
Enhancing Small and Medium OEMs' Adoption of IIoT Technologies
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges of high initial costs, lack of skilled workforce, data security concerns, and limited infrastructure for IIoT implementation. This research explores the crucial factors influencing the successful integration of Industry Internet of Things (IIoT) technologies into products and processes of SME OEMs. The study investigates the impact of IIOT Manufacturers' operational and business support, training effectiveness, and awareness of benefits on SME OEMs' adoption intention of IIoT solutions. A survey was conducted among 263 firms operating in 103 different equipment manufacturing operations across 67 cities, representing 11 industry sectors. The participants were SME OEMs, and data were collected to assess the influence of various factors on their willingness to adopt IIoT technologies. The study revealed significant insights into adopting IIoT solutions among SME OEMs. Training provided by IIoT manufacturers was found to have the most substantial impact on the adoption intention. Moreover, awareness of benefits and business and operational support had an equal and notable influence on the adoption intention of SME OEMs. These findings underline the importance of effective training programs and comprehensive support from IIoT manufacturers in facilitating successful IIoT integration. The study's outcomes emphasize the value of fostering strategic partnerships between Small and Medium Original Equipment Manufacturers and Industry IoT Manufacturers. Such collaborations can be pivotal in enhancing IIoT adoption rates among SME OEMs, enabling them to stay competitive in the fast-paced market. 2024 IEEE. -
Enhancing social cognition in individuals with ADHD: An eastern approach
With the increasing prevalence of ADHD in the global front, it is essential to explore different effective methods for providing support and intervention. The difficulties with social cognition are reflected in their limitations in emotional self-regulation, emotion recognition, and empathy. Though several interventions exist for ADHD, many at times, the effectiveness of eastern approaches are overlooked due to the limited awareness about its nature. Research suggests that systematic and regular practice of yoga helps to improve attention, control emotion, and reduce restlessness among them. Several asanas are found to be especially helpful for managing ADHD symptoms including cobra (bhujangasana) pose, cat-cow pose (bitilasana marjaryasana), downward-facing dog (adho mukha shvanasana), tree pose (vrikshasana), mountain pose (tadasana), among many others. The chapter gives a comprehensive summary on the application of yoga techniques on the improvement of social cognition in individuals with ADHD. 2024, IGI Global. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE. -
Enhancing stochastic optimization: investigating fixed points of chaotic maps for global optimization
Chaotic maps, despite their deterministic nature, can introduce controlled randomness into optimization algorithms. This chaotic map behaviour helps overcome the lack of mathematical validation in traditional stochastic methods. The chaotic optimization algorithm (COA) uses chaotic maps that help it achieve faster convergence and escape local optima. The effective use of these maps to find the global optimum would be possible only with a complete understanding of them, especially their fixed points. In chaotic maps, fixed points repeat indefinitely, disrupting the map's characteristic unpredictability. While using chaotic maps for global optimization, it is crucial to avoid starting the search at fixed points and implement corrective measures if they arise in between the sequence. This paper outlines strategies for addressing fixed points and provides a numerical evaluation (using Newton's method) of the fixed points for 20 widely used chaotic maps. By appropriately handling fixed points, researchers and practitioners across diverse fields can avoid costly failures, improve accuracy, and enhance the reliability of their systems. 2024 Institute of Advanced Engineering and Science. All rights reserved. -
Enhancing Stock Market Trend Prediction Using Explainable Artificial Intelligence and Multi-source Data
Determining the trend of the stock market is a complex task influenced by numerous factors like fundamental variables, company performance, investor behavior, sentiments expressed in social media, etc. Although machine learning models support predicting stock market trends using historical or social media data, reliance on a single data source poses a serious challenge. This study introduces a novel Explainable artificial intelligence (XAI) to address a binary classification problem wherein the objective is to predict the trend of the stock market, utilizing an integration of multiple data sources. The dataset includes trading data, news and Twitter sentiment, and technical indicators. Sentiment analysis and the Natural Language Toolkit are utilized to extract the qualitative information from social media data. Technical indicators, or quantitative characteristics, are therefore generated from trade data. The technical indicators are fused with the stock sentiment features to predict the future stock market trend. Finally, a machine learning model is employed for upward or downward stock trend predictions. The proposed model in this study incorporates XAI to interpret the results. The presented model is evaluated using five bank stocks, and the results are promising, outperforming other models by reporting a mean accuracy of 90.14%. Additionally, the proposed model is explainable, exposing the rationale behind the classifier and furnishing a complete set of interpretations for the attained outcomes. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare
Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE. -
Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network
Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The models performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future. 2023 by the authors.