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An Enhanced Secure Message Authentication Protocol for Internet of Vehicles
Internet of Vehicles (IoV) aims to transform the driving experience to the next level by ensuring communication with other vehicles, pedestrians handheld devices, Road Side Units (RSU), and other sensors used in the smart city environment. It integrates the benefits of the Internet of Things (IoT) and Vehicular Adhoc NETwork (VANET) to offer a safe and comfortable driving environment. Since communication is established through insecure channels, IoV is prone to various security attacks. Hence, there is a need for an authentication mechanism to ensure secure communications. For VANET, various authentication techniques are available, but they are not suitable for IoV due to their high computation overhead. Hence, we suggest a novel secure authentication scheme for IoV, which ensures authentication, conditional privacy preservation, message integrity, traceability, and unlinkability. It also provides security against various attacks. The proposed scheme performs better with less computation, communication, and storage overhead. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
An Enhanced RFM Customer Value-Based Customer Segmentation and Evaluation
Machine Learning Algorithms are widely used in the contemporary era of highly compatible technical improvements to provide answers to the challenges of business environment, yet crucial services for a firm to run successfully in this intensely competitive E-commerce sector. Recently, strategies like clustering and classification mechanisms that allow for the classification of both existing and new clients into clusters have also produced positive outcomes. Recency, Frequency, and Monetary (RFM) measures are hugely being used these days to perform these kinds of tasks. In this study, individual one-dimensional clustering on the Recency, Frequency, and Monetary columns was performed, and a weighted average or preferred linear combination of the three features was then used to calculate an overall score. Summing up the result of three individual clusters. Finally, all of the distinct clients were divided into these three segments based on the overall score, which was divided into three categories. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved. -
An enhanced performance analysis of load based resource sharing framework for MIMO systems in 5G communication systems
Resource sharing serves as a cost-effective and dynamically adjustable method for alleviating traffic congestion in wireless networks. Advancements in multi-input multi-output (MIMO) technologies for 5G communication systems have led to the exploration of resource sharing across various cells or sectors. This approach aims to optimise network performance, focussing on coverage, capacity, and quality of service. This document presents a new load-based resource-sharing framework designed for multi-cell MIMO systems. The proposed framework utilises channel-loading data from local base stations and dynamically allocates available resources among adjacent base stations. The proposed framework facilitates dynamic resource sharing, effectively addressing traffic overload in 5G networks. The proposed LBRS achieved a delta-P value of 90.91%, a prevalence threshold value of 89.84%, a critical success index value of 91.01%, and a Mathews correlation coefficient value of 91.27% at the terminal access. At the resource transmission, the system recorded a delta-P value of 92.10%, a prevalence threshold value of 92.18%, a critical success index value of 91.65%, and a Mathews correlation coefficient value of 88.31%. The simulation results indicate that the proposed framework effectively enhances dynamic resource sharing, resulting in a notable improvement in network performance. The Author(s) 2025. -
An Enhanced Pathfinder Algorithm for Optimal Integration of Solar Photovoltaics and Rapid Charging Stations in Low-Voltage Radial Feeders
Most low-voltage (LV) feeders have large distribution losses, poor voltage profiles, and inadequate voltage stability margins owing to their radial construction and high R/X ratio branches, and they may not be able to handle substantial solar photovoltaics (SPVs) and EV penetration. Thus, optimal integration of SPVs and rapid charging stations (RCSs) can solve this problem. This paper offers an extended pathfinder algorithm (EPFA) with guiding elements and three followers' life lifestyle procedures based on animal foraging, exploitation, and killing. First, the EV load penetration was used to evaluate the LV feeder performance. Subsequently, the required RCSs and SPVs were appropriately integrated to match the EV load penetration and optimise feeder performance. An Indian 85-bus real-time system was used for simulations. The losses and GHG emissions increased by 150% and 80%, respectively, without the SPVs and RCS for zero-to-full EV load penetration. RCSs allocation alone reduced the losses by 40.1%, whereas simultaneous SPVs and RCSs allocation reduced the losses by 66%. However, the GHG emissions decreased by 13.7% and 54.33%, respectively. This study shows that SPVs and RCS can enhance the LV feeder performance both technically and environmentally. In contrast, EPFA outperformed the other algorithms in terms of the global solution and convergence time. The Author(s). -
An enhanced network intrusion detection system for malicious crawler detection and security event correlations in ubiquitous banking infrastructure
Purpose: In the recent era, banking infrastructure constructs various remotely handled platforms for users. However, the security risk toward the banking sector has also elevated, as it is visible from the rising number of reported attacks against these security systems. Intelligence shows that cyberattacks of the crawlers are increasing. Malicious crawlers can crawl the Web pages, crack the passwords and reap the private data of the users. Besides, intrusion detection systems in a dynamic environment provide more false positives. The purpose of this research paper is to propose an efficient methodology to sense the attacks for creating low levels of false positives. Design/methodology/approach: In this research, the authors have developed an efficient approach for malicious crawler detection and correlated the security alerts. The behavioral features of the crawlers are examined for the recognition of the malicious crawlers, and a novel methodology is proposed to improvise the bank user portal security. The authors have compared various machine learning strategies including Bayesian network, support sector machine (SVM) and decision tree. Findings: This proposed work stretches in various aspects. Initially, the outcomes are stated for the mixture of different kinds of log files. Then, distinct sites of various log files are selected for the construction of the acceptable data sets. Session identification, attribute extraction, session labeling and classification were held. Moreover, this approach clustered the meta-alerts into higher level meta-alerts for fusing multistages of attacks and the various types of attacks. Originality/value: This methodology used incremental clustering techniques and analyzed the probability of existing topologies in SVM classifiers for more deterministic classification. It also enhanced the taxonomy for various domains. 2021, Emerald Publishing Limited. -
An enhanced hybrid framework for IoT healthcare security using blockchain-driven multimedia data analysis and cybersecurity techniques
In the era of digital healthcare, safeguarding sensitive patient information while ensuring real-time access and decision-making is paramount. This study presents a novel Hybrid Blockchain-IoT Framework for secure healthcare data management, integrating Elman neural networkbased Blowfish encryption with blockchain and deep anomaly detection. The framework leverages IoT sensor data and utilizes a Proof-of-Authority (PoA) consensus mechanism to ensure tamper-proof transaction recording across decentralized nodes. A Long Short-Term Memory (LSTM) autoencoder combined with a Support Vector Machine (SVM) classifier enables accurate anomaly detection, while cryptographic functions ensure privacy and data integrity. The proposed system is evaluated using a healthcare dataset comprising over 1000 patient records across three network configurations (195, 585, and 1171 nodes). Results demonstrate a Wormhole Attack Probability (%) as low as 1.1%, Product Drop Ratio (%) between 1.2 and 2.7%, and Authentication Delay under 111 msoutperforming existing systems. Although the anomaly detection accuracy (98.98%) and F1-Score (0.90) are slightly below leading deep learning models, our framework uniquely combines encrypted transmission, distributed validation, and intelligent threat detection in a practical healthcare setting. The architecture ensures security, scalability, and efficiency, positioning it as a robust solution for next-generation smart healthcare ecosystems. 2026 The Authors -
An enhanced framework to design intelligent course advisory systems using learning analytics
Education for a person plays an anchor role in shaping an individuals career. In order to achieve success in the academic path, care should be taken in choosing an appropriate course for the learners. This research work is based on the framework to design a course advisory system in an efficient way. The design approach is based on overlapping of learning analytics, academic analytics, and personalized systems. This approach provides an efficient way to build course advisory system. Also, mapping of course advisory systems into the reference model of learning analytics is discussed in this paper. Course advisory system is considered as enhanced personalized system. The challenges involved in the implementation of course advisory system is also elaborated in this paper. Springer Science+Business Media Singapore 2017. -
An Enhanced Deep Learning Model for Duplicate Question Detection on Quora Question pairs using Siamese LSTM
The question answering platform Quora has millions of users which increases the probability of questions asked with similar intent. One question may be structured in two different ways by two users, and answering similar questions repeatedly impacts user experience. Manual filtration of such questions is a tedious task, so Quora attempts to detect and remove these duplicate questions by using the Random Forest Model, which is not completely effective. As Quora contains question answers in the form of text data, different Natural Language Processing techniques are used to transform the text data into numerical vectors. In this research, the log loss metric acts as the primary metric to evaluate different models. The primary contribution is that the Siamese network is used to process two questions parallelly and find vectors representation of each question. The vectors computed by this method enables similarity detection which is more effective than existing models. GloVe word embedding is used to understand the semantic similarity between two questions. The random classifier is built as the base model and logistic regression, linear SVM and XGBoost model are used to reduce the log loss. Finally, a Siamese LSTM is proposed which reduces the loss dramatically. 2022 IEEE. -
An Enhanced Data-Driven Weather Forecasting using Deep Learning Model
Predicting present climate and the evolution of the ecosystem is more crucial than ever because of the huge climatic shift that has occurred in nature. Weather forecasts normally are made through compiling numerical data on from the atmospheric state at the moment and also applying scientific knowledge in the atmospheric processes to forecast on how the weather atmosphere would evolve. The most popular study subject nowadays is rainfall forecasting because of complexity in handling the data processing in addition to applications in weather monitoring. Four different state temperature data were collected and applied deep learning methods to predict the temperature level in the forthcoming months. The results brought out with the accuracy from 92.5% to 97.2% for different state temperature data. 2023 IEEE. -
An enhanced biometric attendance monitoring system using queuing petri nets in private cloud computing with playfair cipher
Every educational institutions needs to analyse and monitor participation. Educationists believe that there should be a fair number of students available in the majority of their classes. In colleges participation is used a measure of consistency. To deal with this kind of a challenging situation, biometric based participation monitoring framework is being proposed. This proposed method with the assistance of face recognition will help in maintaining every detail about the present students in a classroom save the same in the class database. The camera captures the image of students and compares them with the existing visual data available in the database. In case, the software is not able to find a match for the captured data in the student database, the particular student is marked as absent. Queuing Petri nets help in fulfilling customised demands of various institutions along with providing better performance in terms of hold up time. With the application of this technology, classroom participation is recorded and saved every hour. The database is accessed and maintained using cloud services and necessary security measured are incorporated as provided by major private cloud service providers with playfair cipher technique. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
An Enhanced Automation Analysis for Structural Algorithm in Agro-Industries Using IoT
The Internet of Things (IoT) based structural algorithm for automatic agriculture refers to the system of using powerful real-time data collected from a variety of sensors with software and analytics to autonomously manage agro-ecosystems. This algorithm can be used to monitor environments, analyze data and use this knowledge to take specific actions to help farmers and producers maximize their production and profitability. This algorithm provides an unprecedented level of precision, accuracy and control over the agricultural environment, allowing greater efficiency and optimization in farming practices. It enables monitoring, scheduling, and control of different agro-ecosystem components, such as water, soil, fertilizer, light, humidity, temperature, soil pH and crop growth. The algorithm can also point to general trends and patterns in the environment, as well as offer timely advice to farmers in response to real-time conditions. The algorithm is also capable of automatically diagnosing and responding to unexpected problems, which can help prevent costly mistakes and excessive waste of water, fertilizer, energy, etc. 2023 by the authors. -
An Enhanced Approximation Algorithm Using Red Black Tree and HashMap for Virtual Machine Placement Problem
The virtual machine placement problem (VMPP) is an np-hard optimization problem in cloud computing that involves efficiently allocating virtual machines (VMs) to physical hosts in such a way that the resource wastage is minimized, and resource usage is optimal while ensuring adequate performance. This paper proposes a modified best-fit approximation algorithm using Red Black Tree (RBT) and HashMap for addressing the VMPP with enhanced computational efficiency in such a way that the active hosts in a given data center remains minimum possible. The proposed algorithm builds up on the existing best-fit approximation algorithm by using RBT and HashMap. The proposed approach considers various attributes such as CPU utilization, memory requirements, and network bandwidth while allocating virtual machines. To evaluate the performance the simulation is done in cloudsim environment with PlanetLab workload. Test cases are considered in both homogeneous and heterogeneous environments and results are taken. Comparative analyses were performed against existing benchmark algorithms in terms of time complexity and resource usage in terms of active hosts. The results demonstrate that the proposed algorithm outperforms the existing algorithms and guarantees time complexity of O(log n) and give better results compared to other algorithms. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Enhanced A3C-LSTM Framework with Attention for Dynamic Portfolio Allocation in Equity Markets
Portfolio optimization in dynamic financial markets presents a significant challenge for traditional models. This paper introduces an advanced deep reinforcement learning framework for portfolio management based on an enhanced Asynchronous Advantage Actor-Critic (A3C) algorithm. This paper integrate's a Long Short-Term Memory layer and a multi-head attention mechanism into the actor-critic architecture to more effectively capture temporal dependencies and feature importance within financial time-series data. The model's novelty lies in its enriched state representation, which includes a comprehensive set of technical indicators, inter-asset correlation matrices, and market regime analysis. Furthermore, we employ a sophisticated riskadjusted reward function, incorporating penalties for drawdown and volatility alongside a bonus based on the Sortino ratio. The agent was trained and tested in a simulated environment using historical daily price data from five major S&P 500 stocks. Experimental results demonstrate that our agent successfully learns a robust and adaptive allocation strategy, significantly outperforming an equal-weight benchmark in terms of overall return, Sharpe ratio, and maximum drawdown. This study underscores the potential of sophisticated DRL architectures to navigate complex market dynamics and optimize for riskadjusted performance. 2025 IEEE. -
An Energy, Mobility and Obstacle Aware Clustering based Intelligent Routing Protocol for FANET
The use of Flying Adhoc Networks (FANETs), also known as Unmanned Aerial Vehicles (UAVs), has increased in recent years. However, the fast movement of UAVs can lead to unreliable links and inefficient data transmission. To address this issue, the Intelligent-based Energy and Mobility-aware Clustering (IEMC) protocol has been developed, utilizing Battle Royale Optimization (BRO) for Cluster Head (CH) selection and a Deep Q-Learning (DQL)-based fast dynamic hello interval algorithm for path maintenance. Despite these advancements, FANETs still face challenges due to environmental obstacles affecting communication routes. To solve these issues, this article proposes an Intelligent-based Energy, Mobility, and Obstacle-aware Clustering (IEMOC) protocol for FANET routing. This protocol uses an intelligent Bezier route selection technique to deal with obstacles obstructing the paths of FANET nodes and a speed-based mobility prediction technique to reduce the impact of mobility during transmission. If link failure occurs due to an obstacle in the network, the IEMOC protocol selects an optimal alternative routing path via neighboring nodes based on its mobility awareness factor, link duration, network connectivity, and route availability, recovering the failed route without initiating the route discovery process. The effectiveness of the IEMOC protocol is validated through performance evaluations using the Network Simulator (NS)-2.35, and simulation results demonstrate that the IEMOC protocol outperforms conventional routing protocols in FANETs. 2025 The Authors. Published by Elsevier B.V. -
An Energy-aware Dynamic Scheduling Algorithm for Optimizing Workflows Under Budget-constraints
The provision of cloud computing offers an untapping scalability and elasticity which is best suited for the execution of user tasks and complicated scientific workflows. Regardless, the big problem of workflow scheduling under a user-specified budget still prevails as a result of the task inter-dependencies and the resource diversity. This research proposes a hybrid Energy-Aware Enhanced Salp Swarm Algorithm (EA-ESSA), designed to dynamically schedule tasks while adhering to user-specified budget constraints. This technique supports dynamic scheduling using task duplication in idle time spots and integrates APIs for real-time spot pricing. This proposed technique also minimizes makespan and energy consumption by improving resource utilization. The algorithm's performance was exhaustively experimented with using both simulated workloads and actual HPC2N datasets. The simulation results show significant advancements in the makespan, resource utilization and energy consumption compared to existing algorithms like ACO, GA, PSO, and MOTSWAO. This research benefits cloud environments comprising complex, unpredictable workflows by cutting environmental effects and shrinking processing expenses. 2025, Innovative Information Science and Technology Research Group. All rights reserved. -
An Energy Optimized Clustering approach for Communication in Vehicular Cloud Systems
Vehicular cloud networks are considered to possess faster transitional topology and mobility thereby adhering to its features as an ad hoc network. Many times, it is difficult to monitor vehicular nodes that results in internetworking concerns as a result of power inadequacy during real computation. This leads to lots of energy wastage issues encountered during routing which degrades lifetime of nodes. Thus in this study a new clustering based energy optimization method is proposed to enhance the efficiency of vehicular communication. K-medoid cluster analysis along with dragonfly approach is applied to the system model to optimize energy. On the basis of simulation undertaken, it is recorded that the network lifetime, packets delivered, processing delay and throughput are increased using the proposed model. 2023 IEEE. -
An Energy Efficient Node Scheduling based Congestion Control Scheme for WSN Multicasting
Wireless Sensor Network (WSN) is the most preferred technology for communication in resource constrained environments. They offer high-quality data propagation with limited delay. Sensor Network can be established with the help of self-configurable nodes to monitor various physical phenomenon. Multicasting in WSN results in low communication control overhead but may lead to congestion, which results in data loss, redundant transmissions, poor throughput and reduced network lifetime. In this paper, we propose a protocol to estimate the Degree of Congestion (CD) at each node to ensure load balance and avoid further congestion within the network. It is demonstrated that the proposed scheme is better compared with existing congestion control schemes in terms of end-to-end delay and energy efficiency. 2020 G. Raja Vikram et al., licensed to EAI -
An energy efficient authentication scheme based on hierarchical ibds and eibds in grid-based wireless sensor networks
Wireless sensor network is a peculiar kind of ad hoc network, consists of hundreds of tiny, resource constrained as sensor nodes. Clustering is a demanding task in such environment mainly due to the unique constraints such as energy efficiency and dynamic topology. In this paper, a novel energy efficient cluster-based routing algorithm is proposed on which hierarchical identity-based digital signature (IBDS) and enhanced-identity-based digital signature (EIBDS) scheme is concerning in grid-based wireless sensor networks. Firstly we form clusters using multi-parameters-based type-2 fuzzy logic algorithm. This paper proposes an improved ant colony optimisation algorithm, which optimises the energy consumption on data transfer in a WSN. Each node in a sensor network is authenticated using elliptic curve cryptography (ECC). After a set of simulation tests on NS-3 simulator, our proposed work achieves good performance for various metrics. Copyright 2020 Inderscience Enterprises Ltd.