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Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction
Machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector Machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM; as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. 2025 IEEE. -
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 Pneumonia Diagnosis with Convolutional Neural Networks: A Comprehensive Evaluation
Pneumonia, a significant health concern globally, presents unique challenges in diagnosis and treatment due to its diverse ethology and impact on respiratory function. The potential of augmentation techniques and Convolutional Neural Networks, for automated pneumonia detection is explored in this study. Employing a transfer learning approach with VGG16, DenseNet, and our proposed model achieves outstanding accuracy (95%) and robust performance metrics. The research explores augmentation techniques to enhance the precision and accuracy of the model, emphasizing the importance of data augmentation in improving classification accuracy. A comparative analysis with related models highlights advancements in automated pneumonia detection, showcasing the efficacy of our proposed model. The models ability to correctly identify pneumonia from chest X-ray pictures is demonstrated by the results, suggesting that medical image analysis could benefit from practical implementation of this model. Future directions include expanding the dataset, exploring alternative architectures, and integrating explanation techniques to enhance model interpretability. This research contributes to the advancement of artificial intelligence in healthcare, offering a promising approach for accurate and efficient pneumonia diagnosis, thus addressing critical challenges in respiratory medicine. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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 Personalization in Search Engines Through Behavioral Profiling
With the development of search engines, people demand more contextual, relevant, and important results according to their needs and preferences. The current paper will examine the enhancement of search engine personalization through behavioral profiling, which involves capturing user interaction data, such as search histories, clicks, and other similar data, to understand user interests and intentions. The behavioral profiling promotes the ability to adjust the results to the requirements of mutual changes in user behavior and apply machine learning algorithms and advanced data mining techniques. We describe the key aspects of the successful behavioral profiling systems, such as user modeling, data collection frameworks, and privacy boundaries of the data protection. The paper will address the points mentioned by providing behavioral profiling to enhance user satisfaction and effective search and engagement. It will discuss the predictive relevance ranking's triple impacts on socioeconomic gains: time, energy costs, and attention time. We also discuss the ethical issues of user data collection, and the invitation implies achieving the appropriate compromise between individualization and privacy. By the case studies and comparisons, we affirm that the behavioral personalization greatly improves the accuracy of the search when the methods are either static or generic. This study enhances the design of a smart, convenient search engine by cultivating actionable, individual-sensitive recency search. It aims to smoothly aid personalized interactions in real time, inspiring advancement in context-sensitive retrieval systems. The Research Publication,. -
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 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 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 operational efficiency in cloud computing through computational intelligence for environmental sustainability
In the era of generative artificial intelligence technologies, an increasing number of organizations are adopting solutions such as ChatGPT, DeepSeek, Gemini, and Bard, and there has been a significant increase in computationally intensive operations that impact data center operations, as these require a high CPU- and GPU-based infrastructure and a large amount of electricity to run. In this data-driven environment, there is a need to look for and identify a holistic solution to address the concern of managing cloud operations with sustainable practices that can help not only reduce the carbon footprint but also be environment and pocket friendly. Organizations have started adopting smart intelligent technologies and sensors that track and help minimize and prescribe solutions to address the high usage of cloud resources. In this study, we have tried to check the impact of the role of such technologies in optimizing energy consumption, reducing the carbon footprint, and enabling eco-friendly, sustainable practices within cloud operations. While going through the study, you will find that the adoption of new technological solutions that leverage sensors and artificial intelligence-based smart technologies has a profound impact on long-term energy efficiency gains. The study highlights the importance of leveraging computational intelligence-based solutions to drive long-term environmental benefits, while also emphasizing the role of sustainable solutions, such as renewable energy integration and collaborative partnerships, in achieving the desired outcomes. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Enhancing Online Education Through Sentiment Analysis and Complex Systems Modelling
This research explores the application of sentiment analysis through the lens of complex systems modelling to enhance the quality of online certification courses, with a particular focus on global platforms such as Coursera. The COVID-19 pandemic catalyzed significant growth in online learning, creating an urgent need for adaptive and student-centric approaches to ensure relevance and effectiveness. Leveraging unstructured textual data from student reviews of courses, this study integrates methodologies from systems science, computer science, and education to address real-world challenges in online education. By employing both lexiconbased (SentiWordNet and VADER) and supervised machine learning techniques (Multinomial Naive Bayes, Support Vector Machine, and Stochastic Gradient Descent), the research conducts a detailed sentiment analysis to identify patterns, emergent behaviours, and feedback loops inherent in course design and delivery. Findings reveal that Support Vector Machine achieves the highest accuracy at 97.3%, offering insights that guide iterative improvements in course content and pedagogical strategies. The study demonstrates how interdisciplinary approaches to sentiment analysis can inform responsive education environments, aligning with broader societal goals of accessibility, inclusivity, and quality in online learning ecosystems. 2025, Binghamton University Libraries. All rights reserved. -
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 Network Topology with ONOS, P4 Runtime, and BMV2 Switches
The increasing complexity of modern networks demands advanced solutions for efficient and adaptive topology management. Traditional networking approaches, characterized by their rigid and hardware-centric architectures, often fall short in addressing the dynamic requirements of contemporary networks. This paper introduces a novel approach to network topology management by leveraging the Open Network Operating System (ONOS), P4 Runtime, and BMV2 switches. ONOS, a scalable and distributed SDN controller, provides centralized control and a global view of the network, while P4 Runtime offers a protocol-independent interface to manage programmable data planes. BMV2, a versatile software switch, emulates the behavior of P4-programmable hardware, allowing for the development and testing of custom packet processing pipelines. This research lays the groundwork for future developments in programmable networks by bringing out the potential of combining SDN with P4-based data plane programmability to meet the evolving demands of modern network environments. 2025 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 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 Music Emotion Recognition with LSTM: Evaluating Various Embedding Techniques
The study investigates the application of Long Short-Term Memory (LSTM) networks for emotion classification in music lyrics. It focuses on the comparative effectiveness of various word embedding techniques. It evaluates the performance of static embeddings (GloVe, Word2Vec, FastText) versus contextual embeddings (BERT, Distil BERT) across three datasets: MER Lyrics, Mood Lyrics, and Combined Lyrics. Additionally, the study examines the role of stylistic and content-based features in enhancing classification accuracy. The results demonstrate that contextual embeddings considerably outperform static embeddings, achieving accuracy rates of up to 98% compared to 60% for static approaches. Moreover, combining multiple lyric datasets leads to improved model generalization. The findings show the potential of transformer-based models for advancing music emotion recognition. Future research will focus on optimizing large embedding models using techniques such as pruning, quantization, and distillation to enhance computational efficiency. 2025 Seventh Sense Research Group. -
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. -
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 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 Mobile DeFi Transactions Through Blockchain Adoption: A Pythagorean Fuzzy AHP Study
Blockchain technology has emerged as a pivotal enabler for innovation in mobile decentralized finance (DeFi) ecosystems. This study ememploys the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) to identify and rank critical enablers driving blockchain adoption for enhancing mobile DeFi transactions. A structured three-stage methodology evaluates twenty-four sub-criteria across operational, managerial, and strategic dimensions. Results emphasize managerially focused factorssuch as reduced foreign exchange (FX) transfer costs and open-source adaptabilityas the most critical enablers, followed by operational drivers like transparency. Sensitivity analysis confirmed the ro-robustness of these findings. The results offer actionable insights for fintech practitioners, digital strategists, and policymakers seeking to optimize blockchain-based mobile financial platforms. By contributing to improved financial inclusion, operational efficiency, and regulatory alignment, the study supports broader welfare enhancement in the digital economy. The proposed PF-AHP framework provides an empirical decision-making tool to guide innovation and strategic planning in financial technology services. Yogesh Kumar Jain et al. -
Enhancing Mobile Application Security Through Android Threat Classification
The Android application market has grown significantly, offering customers an ever-growing range of features to suit a variety of purposes. Users are exchanging more and more sensitive data thanks to the widespread usage of mobile applications, therefore safeguarding personal information is crucial. But this boom has also opened the door for a corresponding rise in cybersecurity risks, especially for malware and adware that target mobile devices. It is imperative to categorise mobile applications into distinct groups such as malware, adware, and benign in order to fortify the mobile ecosystem. This project's primary objective is to create and apply cutting-edge machine learning algorithms that can precisely categorise mobile apps into groups including adware, malware, and benign apps. This will necessitate investigating various machine learning strategies and ensemble methods to improve classification accuracy and robustness. Multiple machine learning models were developed based on feature importance, utilizing various machine learning techniques. The evaluation metrics showcase the effectiveness of the final model, especially the Tuned XGBoost model. While achieving a high overall accuracy of 92.51%, the findings highlight the importance of considering diverse features beyond traditional flow-based ones, providing a more robust and complete perspective on mobile network security. 2025 The Authors. Published by Elsevier B.V.