International Journal of Current Trends in Engineering and Technology
International Journal of Current Trends in Engineering & Technology (IJCTET) is a leading international online journal for publication of new ideas. It's a peer-reviewed, open-access scholarly journal that publishes research works and review articles in all research domains (Engineering, Technology, Science, Agriculture, Horticulture, Pharmacy, Medical, and Management). IJCTET is an open-access journal which means that all content is freely available to the Researcher /users of any institution with vision spreading and sharing knowledge. These published articles/papers are accessible to the research scholars through an extensive indexing policy adopted by this online international journal. Hence, they can freely be accessed and used by everyone for the improvement of current trends in science and technology. This Journal favors and promotes online publication of papers to present itself as an online journal indeed. IJCTET follows publication ethics during phases of online publication inline with the guidelines and standards developed by the Committee on Publication Ethics (COPE) to avoid any malpractices by all concerned including authors, reviewers, editors, and publishers. IJCTET is following the BOAI definition of open access. We are committed and promise to take this journal to greater heights. We invite authors for the guest editorship, reviewers, program for improving the journal quality and also sharing the innovative ideas. Frequency : Six issues/year Submit Article at editor[AT]ijctet[DOT]org
Utilization of CO2 from a Carbon Capture Plant for Curing Alkali-Activated Low-Reactivity Fly Ash
Nowadays, the carbon dioxide (CO₂) capture and utilization for curing alkali-activated materials offer a promising pathway for both CO₂ sequestration and the production of low-carbon construction materials. However, low-reactive fly ash (FA), which is an easily accessible and widely available industrial byproduct, poses basic challenges due to its poor reactivity under conventional curing conditions. In this research, the utilization of CO₂ generated from a pilot-scale carbon capture plant at RKDF University, Bhopal, is investigated to cure alkali-activated low-reactive FA pastes. The curing process is carried out at 40 °C and 0.5 bar gauge pressure for durations of 6, 12, and 24 hours. Mass monitoring, compressive strength testing, X-ray diffraction (XRD), and efflorescence assessment are employed to evaluate the performance. The experimental results show that after 24 hours of CO₂ curing, the pastes achieved a CO₂ uptake of 12.4% by mass of FA. After 28 days, a compressive strength of 42.6 MPa is found, which is approximately 50% higher than that of ambient-cured controls. Efflorescence is nearly eliminated due to the conversion of free alkalis into insoluble carbonates. XRD analysis confirmed the formation of calcite (CaCO₃) as the primary carbonation product, while quartz and mullite remained inert. This research experimentally shows that CO₂ from a carbon capture plant can effectively upgrade low-reactive FA into a high-strength, durable material, offering benefits of waste valorization, CO₂ storage, and utilization.
Copyright (c) 2026 International Journal of Current Trends in Engineering and Technology
Optimizing User Pairing in NOMA Using K-Medoids Clustering for Enhanced Spectral Efficiency
Non-Orthogonal Multiple Access (NOMA) has emerged as a pivotal technology for 5G and beyond wireless networks, which promises the significant enhancements in spectral efficiency through power-domain multiplexing. However, the practical realization of benefits of NOMA critically depends on solution of the complex user pairing problem, which determines which users should be grouped for simultaneous transmission. This paper basically addresses the critical user pairing challenge in NOMA systems by proposing a novel K-Medoids clustering algorithm. The proposed algorithm optimizes user pairing based on a comprehensive feature vector incorporating channel gain magnitude, angular separation, and potential the signal-to-interference-plus-noise ratio (SINR). The pairing problem is formulated as a clustering optimization with a custom distance metric that balances the channel gain differences and spatial characteristics. The simulation results show the proposed method achieves higher spectral efficiency than the exhaustive search, random pairing, and channel gain difference (CGD), and reduces complexity. The proposed algorithm maintains a Jain's index at excellent level. The proposed algorithm demonstrates robustness across varying user densities while maintaining excellent fairness.
Ant Colony Optimization-Based Energy Efficient Clustering and Routing in Wireless Sensor Networks
One of the major challenges in wireless sensor networks (WSNs) is extending the network's operational lifetime, given the limited battery power of sensor nodes. These nodes perform essential tasks such as sensing, processing, routing, and transmitting data to the base station, all of which consume significant energy. This Research reviews current energy-optimized routing protocols and evaluates their performance against a newly proposed method. We examine several well-known protocols—LEACH, DEEC, DDEEC, EDEEC, and EDDEEC—which employ their own algorithms for energy efficiency, often using probability-based cluster head (CH) selection. However, this probabilistic approach can result in energy imbalance, where nodes with low energy may be chosen as CHs while high-energy nodes are overlooked, ultimately affecting network longevity. Furthermore, these protocols often do not account for the real-time energy levels of nodes during CH selection. To address these limitations, this study introduces an Ant Colony Optimization (ACO)-based routing protocol inspired by the natural behavior of ants. This method incorporates energy level awareness during CH selection and employs a dual-cluster-head approach within each cluster to enhance network performance. Simulation results demonstrate that the proposed algorithm significantly improves network lifetime and throughput.
Copyright (c) 2025 International Journal of Current Trends in Engineering and Technology
Advanced Ransomware Detection Framework using Memory Forensics and Deep Learning
Ransomware has emerged as one of the most pervasive and damaging cybersecurity threats, with attacks targeting individuals, corporations, and critical infrastructure. Traditional detection techniques such as signature-based and heuristic methods often fail to identify novel or obfuscated ransomware variants, especially those employing polymorphic and zero-day tactics. This research proposes an \textbf{Advanced Ransomware Detection Framework} that synergizes digital forensics with deep learning techniques to detect and classify ransomware effectively. The framework begins with the forensic acquisition of behavioral data, including system calls, registry modifications, file system changes, and entropy analysis. These artifacts are then processed to extract static and dynamic features that capture both execution patterns and contextual anomalies. The results demonstrate that the fusion of forensic intelligence and deep behavioral modeling offers a powerful and scalable solution for ransomware detection, promising real-world applicability in enterprise and cloud security environments.
Dynamic Resource Management and Load Balancing in Cloud Computing using Efficient Hierarchical Clustering Techniques
The cloud computing environments demand efficient resource management and load balancing to optimize performance, scalability, and cost-effectiveness. This research proposes a novel approach to dynamic resource management and load balancing using efficient hierarchical clustering techniques. By leveraging the hierarchical clustering, the proposed framework dynamically groups cloud resources based on workload patterns, resource utilization, and system demands. This employs a multi-level clustering strategy to categorize virtual machines and tasks, which enables the adaptive resource allocation and load distribution. Simulations results demonstrate that the proposed technique significantly improves resource utilization, reduces response times, and enhances system scalability compared to traditional load balancing algorithms. The proposed approach also minimizes the energy consumption and operational costs, which makes it suitable for large-scale cloud infrastructures. This research contributes to advancing cloud computing efficiency, which offeres a scalable and robust solution for dynamic resource management in heterogeneous cloud environments.
Bio-Inspired Optimisation and Deep Learning Approaches for Diabetes Prediction: A Comprehensive Survey
Diabetes mellitus, a chronic metabolic disorder, poses a growing global health challenge requiring timely and accurate diagnosis. Traditional diagnostic methods often fall short due to their manual and error-prone nature. Recent advancements in machine learning (ML), deep learning (DL), and bio-inspired optimisation have significantly enhanced the potential for early diabetes prediction. This survey presents a comprehensive overview of state-of-the-art techniques integrating ML/DL models with nature-inspired algorithms for improved diagnostic accuracy and feature selection. It examines traditional classifiers, ensemble methods, and deep architectures like CNNs and LSTMs, highlighting their suitability for complex clinical data. Bio-inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Shuffled Frog Leaping Algorithm (SFLA) are discussed for their effectiveness in dimensionality reduction and model tuning. Additionally, the paper covers essential preprocessing steps, including handling missing data, outliers, class imbalance, and feature scaling. Explainable AI (XAI) methods, evaluation metrics, and cross-validation strategies are explored to ensure model transparency and reliability. The survey also identifies emerging trends such as federated learning and Bayesian deep learning, outlining future directions for building trustworthy, scalable, and clinically applicable diabetes prediction systems.
Improved Robust, Invisible Digital Image Watermarking Using Discrete Cosine Transform and PM
With technological advancements, the internet has become the primary medium for transferring information across the globe. This medium enables quick, easy, and accurate data transmission. However, transmitting information over the internet also presents significant security threats. In this context, digital watermarking has emerged as a promising technique for ensuring data authentication and integrity. In the proposed approach, the source image is processed in row-major order, and the Discrete Cosine Transform (DCT) is applied to generate low and high-frequency components. One bit of the authenticating image is embedded into each transformed DCT coefficient, excluding the first coefficient, to maintain low perceptual distortion and enhance PSNR. Early work on digital watermarking, including analyses of various schemes and their applications, is discussed to provide background. Unlike many existing methods that emphasize content authentication over strict integrity, this paper introduces a binary value-shifting technique based on the statistical characteristics of image pixels. This method embeds watermark data into the cover image to improve PSNR and enhance image protection. The implementation is carried out on the Windows platform using MATLAB. The proposed technique aims to secure digital images while achieving improved PSNR values. Digital watermarks are also used to verify the reliability and integrity of the signal or image data. The effectiveness of the approach is evaluated using normalized correlation to assess robustness against various attacks.
Study on Energy-Efficient Aware Localization WSN and Secure Model for WSN Lifetime Enhancement Satendra Ahirwal
Wireless sensor technologies and standards for wireless communications or wireless sensor network localisation are important areas that have attracted significant research interest. This interest is expected to grow further with the increase in the number of wireless sensor network applications. The location of sensors remains unknown to most of the sensors themselves; this is a result of the limitations created by cost, energy consumption, sensor size and deployment, and the environment for implementation. Sensor network algorithm estimates the coordinates of nodes. Localisation is one of the key techniques in wireless sensor networks. The location estimation methods can be classified into target node and source node localisation and node self-localisation. Existing methods (RSS, AOA, TOA, TDOA) simulate analysis errors more. Still, our proposed methods are better in terms of localisation accuracy and minimisation of error rate than the existing localisation method. Range-based schemes compared the localisation positioning method (RSS) and proposed scheme analysis in wireless sensor networks supported by the Internet of Things. In the Internet of Things, using the localisation positioning method and finding the correct position of the number anchor nodes, satisfying each of the factors in WSN, minimises error. Another practical issue involving the presence of malicious sensors called Byzantines is discussed, and mitigation schemes are provided. A recent coding-theory-based approach, which is both computationally inexpensive and robust to such malicious attacks, is also discussed.
A Survey of Artificial Intelligence-Driven Flood Risk Assessment and Forecasting with Remote Sensing Data
Floods are among the most devastating natural disasters, causing significant damage to lives, property, and infrastructure. Traditional flood prediction methods, such as hydrological models and statistical approaches, often struggle with real-time data complexity and environmental variability. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionised flood risk assessment and forecasting by leveraging remote sensing data, IoT networks, and satellite imagery. This survey comprehensively reviews AI-driven flood prediction techniques, highlighting the efficacy of models like Random Forests, Support Vector Machines, and deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The study also explores ensemble methods, hybrid models, and metaheuristic optimisations to enhance prediction accuracy. Challenges such as data scarcity, computational constraints, and model interpretability are discussed, along with emerging solutions like quantum machine learning and semi-subjective analytic frameworks. The review underscores the transformative potential of AI in flood management, particularly in regions like India, where climate variability and rapid urbanisation exacerbate flood risks. By synthesising the latest research, this paper provides insights into future directions for improving flood forecasting systems and mitigating disaster impacts.
Fake News Detection Using Machine Learning: A Comparative Analysis of Algorithms
With the rapid proliferation of fake news, distinguishing between real and fabricated information has become a critical challenge in the digital age. Fake news not only manipulates public opinion but also poses a significant threat to social stability. This study explores the application of machine learning techniques for the automatic detection of fake news articles. A labelled dataset comprising both genuine and fake news is utilized, incorporating preprocessing techniques such as data cleaning, text normalization, and feature extraction. The research implements and evaluates multiple machine learning models, including Logistic Regression, Naïve Bayes, and Support Vector Machines (SVM), comparing their performance based on standard evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that machine learning models significantly enhance fake news detection, with DistilBERT achieving a state-of-the-art accuracy of 99.90%, outperforming traditional approaches. The study underscores the effectiveness of Natural Language Processing (NLP)-based techniques in improving classification accuracy. The findings contribute to the growing body of literature on automated misinformation detection and highlight potential avenues for further research, such as integrating metadata-based features, ensemble learning approaches, and real-time detection systems. These insights serve as a foundation for developing more robust and scalable solutions for combating misinformation in digital media.
Impact of Artificial Intelligence and Automation on HR Functions: Opportunities and Challenges
Artificial Intelligence (AI) and automation are transforming Human Resource (HR) functions, redefining traditional processes and enhancing operational efficiency. This paper delves into the opportunities and challenges associated with AI-driven HR practices, with a particular focus on recruitment, talent management, performance evaluation, and employee engagement. AI-powered tools streamline candidate screening, optimize workforce planning, and enable data-driven decision-making. However, concerns regarding bias in AI algorithms, data privacy, and job displacement present significant challenges. The study also highlights the ethical considerations of AI integration in HR, emphasizing the need for transparency, fairness, and compliance with labour laws. Moreover, as AI reshapes HR roles, professionals must develop new competencies, including data analytics, technological adaptability, and strategic decision-making skills. This research underscores the importance of balancing automation with human-centric approaches to foster a more inclusive, ethical, and effective HR ecosystem in the digital era.
Study of the Balance between Personalized Marketing and Consumer Data Privacy
Personalized marketing has revolutionized business-consumer interactions by leveraging data analytics and artificial intelligence to create tailored experiences. This approach enhances customer satisfaction, fosters brand loyalty, and improves marketing efficiency. However, the extensive collection and utilization of consumer data have led to significant privacy concerns. The risks associated with data breaches, unauthorized sharing, and lack of transparency in data usage raise ethical and legal questions. This research paper examines the balance between personalized marketing and consumer data privacy, highlighting the benefits, challenges, and ethical dilemmas inherent in this practice. While consumers appreciate customized experiences, they also express concerns about excessive tracking and potential misuse of their personal information. The "personalization-privacy paradox" reflects the conflict between the demand for personalized services and the need for data security. Regulatory frameworks such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) aim to protect consumer rights while allowing businesses to harness data responsibly. This study explores strategies that companies can adopt to achieve a balance, including transparency, informed consent, data minimization, and robust security measures. By prioritizing ethical data practices, businesses can build consumer trust while maintaining effective personalized marketing strategies.
Assistive Technology Solution for Visually Impaired/Low Vision Students for Their Schooling
Nowadays, visually impaired students face many challenges during their schooling. Due to their low vision, they are unable to compete with others to their full potential. In certain situations where visual attention is necessary, such as chemistry lab practicals, reading notices on notice boards, and detecting hurdles in their path, these students encounter significant difficulties. Therefore, our intention is to assist students with visual impairments or low vision in their schooling through the use of assistive technology. Assistive technology refers to any device, software, or equipment that helps individuals overcome their challenges. This report aims to discuss the development of assistive devices that will serve as effective solutions for visually impaired students.
Optimizing Concurrency in Heterogeneous Data-Parallel Applications – An Automated System Approach
The existing high-performance computing (HPC) frameworks lie in their suboptimal utilization of the inherent concurrency in data-parallel applications. While these frameworks provide high-level abstractions, their scheduling decisions often fail to fully exploit the potential for concurrency within heterogeneous CPU and GPU architecture. In order to address this limitation, this article proposes a novel framework designed with a philosophy akin to other high-performance computing frameworks but with a distinct emphasis on exploring fine-grained concurrency-aware scheduling decisions. The primary objective is to harness the complete computational power of heterogeneous CPU and GPU architectures.
Copyright (c) 2024 International Journal of Current Trends in Engineering and Technology
Improved Data Analysis Using K-Means Algorithm with Quantum Particle Swarm Optimization Algorithm and PVSFCA
Data analysis based on the K-Means algorithm has been enhanced by integrating the Quantum Particle Swarm Optimization (QPSO) approach and the proposed Vector Space Function Clustering Algorithm (PVSFCA). Data mining aims to extract valuable insights from vast datasets and present them in an understandable format for further utilisation. Clustering involves grouping objects so that objects within the same cluster exhibit greater similarity to each other than those in other clusters. K-Means clustering involves partitioning a dataset into K clusters. This study explores the significance and prevalence of data mining techniques, investigates clustering’s role in data mining, and delves into the characteristics, fundamental principles, and execution process of the K-means algorithm. Although K-means clustering is widely used for its simplicity, efficiency, and empirical success, classic K-means has shortcomings like predefining K, random initial centre selection, and more, impacting its performance. Numerous variations of K-means have emerged to address these limitations. K-means is often utilised to minimise the squared distance between feature values of points within the same cluster. The Quantum Particle Swarm Optimization (QPSO) algorithm, integrated with K-means (QPSO-K-means), is an evolutionary computation technique to find suboptimal solutions in various scenarios. This approach simulates cluster centres as particles to obtain stable and suitable clusters, leading to effective clustering outcomes. The proposed algorithm (PVSFCA) is analysed using the UCI healthcare dataset, demonstrating its efficiency and accuracy. The algorithm’s outcomes provide a reliable foundation for enhancing clustering strategies by considering factors such as iterations, error rate, and the optimal creation of cluster centres.
A Novel AWS Cloud Computing Algorithm of Security and Privacy
With the increasing integration of cloud computing into modern technology ecosystems, ensuring robust security and maintaining user privacy within cloud environments has become a critical concern. This abstract presents an innovative algorithm developed to address these challenges, specifically within the Amazon Web Services (AWS) cloud computing platform. The proposed algorithm, the Secure AWS Algorithm, seamlessly incorporates state-of-the-art AWS services, advanced encryption techniques, access control mechanisms, and data anonymisation methods to establish a comprehensive framework for bolstering security and privacy. The Secure AWS Algorithm adheres to fundamental security principles by employing components such as Virtual Private Cloud (VPC) isolation, Identity and Access Management (IAM) policies, multi-factor authentication (MFA), and Intrusion Detection and Prevention Systems (IDS/IPS). Moreover, robust encryption measures ensure data protection during storage and transmission. The algorithm employs data masking, anonymisation, and encryption techniques to safeguard user privacy, effectively mitigating the risk of unintended exposure of sensitive information. Through an exhaustive comparative analysis, this abstract highlights the distinct advantages of the Secure AWS Algorithm over existing methodologies. The efficacy of the algorithm is assessed across key parameters, including accuracy in threat detection, strength of data encryption, and configuration of access controls. The results of the evaluation underscore the considerable potential of the Secure AWS Algorithm in significantly enhancing security and privacy within AWS cloud environments. This advancement paves the way for a more secure and privacy-conscious landscape in the realm of cloud computing.
Copyright (c) 2023 International Journal of Current Trends in Engineering and Technology
An Analytical Study of Active and Reactive Powers of STATCOM with PI and DSM-PI Controllers
Electricity generation from wind and solar photovoltaic (PV) systems relies heavily on prevailing weather conditions. Their intermittent output nature leads to fluctuations. As a result, there is an increasing need for swift compensation within energy transmission and distribution systems. One solution is the adoption of a Static Synchronous Compensator (STATCOM) for reactive power compensation, aiming to mitigate voltage fluctuation stemming from the system and renewable energy sources. This research delves into modelling a Solar PV-Wind Hybrid Micro-grid and explores how incorporating STATCOM can extend the stable operating limit of the system. A significant contribution of this study lies in implementing sliding control of Proportional Integral (PI) gains through DSM-PI (Dual Sliding Mode Proportional Integral) methodology. This approach yields improved responses and enhanced voltage stability, effectively addressing the nonlinear attributes of solar-wind hybrid microgrids. The Simulink models of the system architecture encompass a 1.5 MW wind turbine model utilising a doubly fed induction generator (DFIG), a 0.1 MW solar PV power system model, and a 3 MVAR-rated STATCOM. The results showcase an 8% reduction in voltage fluctuation at the bus bar’s end using a conventional PI controller. A comparative analysis between the outcomes of the DSM-PI controller and the conventional counterpart demonstrates superior performance achieved through the DSM-PI approach.
Exploration and Categorization of Electrocardiogram Signals through the Application of Edge Computing
This article presents a comprehensive analysis and categorization of Electrocardiogram (ECG) signals, leveraging the power of edge computing. The study focuses on enhancing the performance of a 12-channel ECG arrhythmia categorization system by implementing advanced oversampling and undersampling techniques. Notably, Random Oversampling (ROS) emerges as particularly effective, demonstrating significant improvements over models trained with original data. Despite commendable performance across various cases, ROS stands out for its straightforward implementation, making it a preferred choice for robust ECG signal categorization. The findings contribute to the refinement of ECG signal processing methodologies, especially in the context of edge computing applications.
Automated Derivation of Marketing Analytical Insights from Social Media Information
In contemporary digital marketing, effective audience segmentation is crucial at both aggregate and individual user levels. Personas, representing fictional user profiles, serve to humanize data, offering distinct identities to audience segments. Marketers utilize personas to gain deeper insights into their customers and make efficient marketing decisions for each segment. Developed within the realm of big data analytics and artificial intelligence, this article proposes a solution that not only demonstrates the viability of automatic persona generation from social media data but also illustrates the design and implementation of a highly scalable, expandable, and accurate system for this purpose. The proposed solution comprises various components, each assigned specific tasks, including data collection, enrichment, clustering, and persona generation. Multiple classifiers are employed to extract insights from the data, subsequently utilized to create customer segments. The integration of these components into a stream-processing architecture mitigates bottlenecks, ensuring a high level of separation of concerns. The final outcome reveals user segmentation with exceptional accuracy, precision, and recall measures, all exceeding 0.90%.