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Enhanced Autism Prediction using Hybrid Machine Learning Model
Autism Spectrum Disorder (ASD) is a condition where individuals face challenges in neurological development and have verbal, non-verbal, learning and behavioral disorders. Even though this condition is identifiable in the first few years in the children's life, many remain undiagnosed until later. This leads to long term developmental issues and delayed interventions. This is what makes the early detection critical for improving development in children. Despite that, traditional diagnosis approaches like behavioral checklists and pre structured interviews rely on the clinician's expertise and are time consuming and have a risk of inconsistency. This study entails and addresses the above problem by proposing a machine learning based multi model to automate early detection in toddlers aged 12 to 36 months. In the initial stage, the traditional classification algorithms like Logistic Regression, SVM are evaluated with high accuracy, F1 score. Then, hybrid models are developed by combining Gradient Boosting as the anchor model with other high performing algorithms, to overcome the limitation of single classification models. These hybrid models help to overcome the limitations of the individual classifiers. Finally, the best-performing hybrid model is enhanced further by Hyperparameter tuning, Feature selection and Cross validation. The outcome of this research will be a hybrid model, combining machine learning algorithms with the best scores, ensuring high accuracy and low false positives. This aims to help in the detection of ASD in early stages in toddlers. 2025 IEEE. -
Enhanced Automated Online Examination Portal Using Convolutional Neural Network
In recent years, the digital evolution of education has significantly shaped the landscape of learning, steering it away from traditional classroom settings towards more agile e-learning platforms. This shift has underscored the urgency for comprehensive online examination systems, tailored to meet the unique challenges and demands of virtual education. Online learning platforms have seen a rapid rise in popularity, given their flexibility, cost-effectiveness, and capability to cater to learners worldwide. Such a widespread audience brings along the challenge of conducting exams without the constraints of geography and scale. Traditional examinations, with their manual paper based formats, fail to fit within this digital mold due to their logistical challenges and inefficiencies. Consequently, an online examination system not only introduces convenience but also operational efficiency, eliminating many of the logistical nightmares associated with manual exams. While existing tools might provide online testing capabilities, the integration of Artificial Intelligent driven proctoring in this portal elevates the standards of academic integrity to unprecedented levels. The main aim of this article is to create online test platform with the support of Artificial Intelligence technology. The result detect the malpractice activity and electronic device usage detection while online examination. 2023 IEEE. -
Enhanced Automated Oxygen Level controller for COVID Patient By Using Internet of Things (IoT)
The Internet of Things (IoT) shall be merged firmly and interact with a higher number of altered embedded sensor networks. It provides open access for the subsets of information for humankind's future aspects and on-going pandemic situations. It has changed the way of living wirelessly, with high involvement and COVID-related issues that COVID patients are facing. There is much research going on in the recent domain, like the Internet of Things. Considering the financial-economic growth, there isn't much significance as IoT is growing with industry 5.0 as the latest version. The newly spreading COVID-19 (Coronavirus Disease, 2019) will emphasize the IoT based technologies in a greater impact. It is growing with an increase in productivity. In collaboration with Cloud computing, it shows wireless communication efficiently and makes the COVID-19 eradication in a greater way. The COVID-19 issues which are faced by the COVID patients. Many patients are suffering from inhalation because of lung problems. The second wave attacks mainly on the lungs, where there is a shortage of breathing problems because of less supply of oxygen (insufficient amount of oxygen). The challenges emphasized as proposed are like the shortage of monitoring the on-going process. Readily being active in this pandemic situation, the mentioned areas are from which need to be discussed. The frameworks and services are given the correct data and information for supply of oxygen to the COVID patients to an extent. The Internet of Things also analyzes the data from the user perspective, which will later be executed for making on-demand technology more reliable. The outcome for the COVID-19 has been taken completely to help the on-going COVID patients live, which can be monitored through Oxygen Concentration based on the IoT framework. Finally, this article discusses and mentions all the parameters for COVID patients with complete information based on IoT. 2022 IEEE. -
Enhanced Battery Life with Supercapacitor Applied to Renewable Energy Based Electric Vehicles
The main goal of this work is of developing a control approach, which is able to obtain the smooth switching between energy sources, battery, and Supercapacitor (SCAP). With four separate math functions, a new math function-based (MFB) controller is designed, and this MFB will generate four output signals corresponding to the motor's speed. Further, the MFB is combined with an FLC/PI controller to reach the theme of the work. Two-hybrid different controllers are intended as per the proposed control strategy termed as MFB with FLC and MFB with PI and both are implemented individually in MATLAB/Simulink in four different modes. The entire model is implemented including a solar panel to charge the battery, this solar panel (SP) is connected to the battery and UDC through various control switches. Finally, a comparative analysis is made between two hybrid controllers to know the better-performed controller. 2023 Ecole Polytechnique de Montreal. All rights reserved. -
Enhanced Channel Division Method for Estimation of Discharge in Meandering Compound Channel
Accurate prediction of shear force distribution along the boundary in open channels is a key to the solution of numerous hydraulic problems. The problem becomes more complicated for meandering compound channels. A model is developed for predicting the percentage of shear force at the floodplain (%Sfp) of two-stage meandering channels using gene-expression programming (GEP) by considering five dimensionless parameters viz. the width ratio, relative depth, sinuosity, bed slope, and meander belt width ratio as the inputs in the model. Basing on the %Sfp, the apparent shear force along the division lines of separation in compound channels is selected for discharge calculation using the conventional channel division methods. An Enhanced Channel Division Method (ECDM) is introduced to calculate discharge by assuming interface line at main channel and floodplain junction. A modified variable-inclined (MVI) interface is suggested having zero apparent shear determined from flow contribution in the main channel and floodplain. The MVI interface is further used to calculate discharge in the meandering compound channels. Performance of the GEP model is tested against other analytical methods of calculating %Sfp. Error between the observed and calculated discharges using the MVI interface is found to be the minimum when compared to other interface methods. The enhance channel division method is successfully applied for validating the two available overbank discharge values for the river Baitarani at Anandapur (drainage area of 8570 sq. km), giving the minimum errors of 0.31% and 1.02% for flow depths of 7.5m and 8.63m, respectively. 2020, Springer Nature B.V. -
Enhanced Cloud Security: Certificateless Public Auditing Using LBMHT for Malicious TPA Detection
Leveraging the Lattice-Based Merkle Hash Tree (LBMHT), the paper presents a certificate-less publicly auditing technique that targets hostile third-party auditors (TPAs) in cloud settings. Without the usual burden of certificate-heavy and certificate-management-prone identity-based or encrypted with public keys structures, this method seeks to improve information safety and integrity. To enhance the effectiveness of the Key Generation Centre (KGC), decrease complexity of space, and optimise storage in the cloud, the suggested solution utilises multi-ciphertext searching using lattice-based, certificate-less verification. The concept guarantees collision-free hashes by using a Merkle Tree structure, which makes it effective for information confirmation. Based on simulation findings, LBMHT is superior to current AES and RSA methods in terms of performance, decreasing executions, encryption, and decryption durations while simultaneously decreasing communication expenses, responding duration, and memory utilisation. The suggested approach is more economical with resources and works well in scaled cloud settings because of its increased accuracy, less effect from malevolent attackers, and improved throughput. At the end of the section, we go over the benefits of the framework, which include reduced utilisation of resources and validated indicators of performance. Compared to Rivest-Shamir-Adleman (RSA) and Advanced Encryption Standard (AES), the suggested LBMHT method has a higher overall accuracy of 99.4 percent. The efficiency of LBMHT in securely organising and analysing data is shown by its great accuracy. 2026 American Institute of Physics Inc.. All rights reserved. -
Enhanced Data Security Architecture in Enterprise Networks
Encryption and storing important information is one of the risky and most challenging tasks. It is the need of the hour in todays fast growing technological transformations that the world is undergoing. A simple Enterprise network is the communication backbone of any organization. It mostly provides better information storage and efficient retrieval, which helps the organization to function smoothly, without having to think twice about their crucial datas security aspects. The information technology paradigm, cloud computing is used to help the organization to focus on its core business. In cloud computing is dealing with many services. That service is used for provide Platform service with infrastructure and software service. This paper, promotes the idea of combining various security and encryption algorithms to connect different enterprise networks using cloud computing, security layer concepts and giving no room for hackers to intrude into the confidential system of data. Springer Nature Switzerland AG 2020. -
Enhanced Design and Performance Analysis of a Seven-Level Multilevel Inverter for High-Power Applications
The structure and performance analysis of a seven-level multilevel inverter is discussed in this study. Due to their capacity to get around the drawbacks of traditional two-level inverters, like high voltage stress on power devices and harmonic distortion, multilevel inverters have attracted a lot of attention lately. Multiple voltage levels can be produced by the seven-level multilevel inverter which is being proposed because it uses a sequential arrangement of power sources and capacitors. The design methodology involves selecting appropriate power devices and capacitance values to achieve the desired voltage levels while minimizing losses and ensuring reliable operation. Total harmonic distortion (THD), inverter efficiency, and voltage stress on power devices are all considered as part of the performance analysis. In comparison to conventional two-level inverters, simulation results indicate that the proposed seven-level multilevel inverter offers lower THD, increased efficiency, and reduced voltage stress. This research contributes to the advancement of multilevel inverter technology and its potential applications in various power conversion systems. 2023 IEEE. -
Enhanced Detection of Malicious URLs Using Supervised Machine Learning Models
This paper deliberates on URL phishing, one important subset of cyber threats. Most modern-day deceptive practices have shifted to the digital space due to the vast scope of information available on the internet. URL phishing is a dishonest practice that includes masquerading harmful links as legitimate links to trick users into sharing their private data. Detection of URL phishing is extremely challenging, hence most of these attacks go undetected until it is too late for the victim. Automatic blacklist that rely heavily on user-generated reports to monitor internet links have been repeatedly proven ineffective time and again. Along with failing to identify newly listed phishing sites, these systems also tend to mistake harmless links for phishing traps. This paper proposes the application of classification techniques of practical machine learning, specifically analysing the patterns and behaviours of URLs to detect phishing websites accurately. Leveraging the properties of Decision Trees, Random Forests, Logistic Regression, SVM, and Light GBM, we were able to come up with a detection model, which precisely calculates accuracy, precision, recall, as well as F1 score to evaluate the validity of URL classification. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Enhanced dielectric and supercapacitive properties of spherical like Sr doped Sm2O3@CoO triple oxide nanostructures
Integrating the hybrid nanostructures exhibiting enhanced storage and electrical properties requires tuning of composition of constituents. To address this issue, we prepared Sr2+ nanoparticles (NPs) decorated over Sm2O3@CoO nanostructures (NS) by chemical precipitation. The structure integrity of the composite was determined by analytical tools. Based on the strongest peak of X-ray diffraction (XRD), crystallite size of the nanoparticles was determined to be 26.14 nm, indicating a mixed phase of monoclinic and tetragonal crystal formation. FESEM revealed a spherical-like morphology with a homogeneous distribution of microstructures with average sizes ranging from 68 nm to 60 nm. The optical absorptivity revealed a redshift in absorption bands centred at 337.0 nm, 343.9 nm, and 353.0 nm in UV-region. The optical band gap of NS was found to be in the range of 3.38 eV to 3.15 eV, and the BET surface area of Sr15%:Sm2O3@CoO was found to be 458469 cm2/g with a corresponding pore size of 13.17 nm. All Sr-doped Sm2O3@CoO NS exhibited higher ionic conductivity and dielectric constant than undoped material. In an aqueous KOH electrolyte, the NS showed a specific capacity of 234.2C/g (65.1mAh/g) demonstrating the material as potential candidate in energy storage and dielectrics. 2022 Elsevier Ltd -
Enhanced Digital Image Watermarking Using 3-Level Discrete Wavelet Transform (DWT)
This study compares the algorithm's performance to that of the DWT level 1 and level 2 techniques while proposing a digital picture watermarking technology using a 3-step Discrete Wavelet Transform (DWT). The suggested method uses alpha blending to overlay a multibit watermark into the frequency subband of the lower cover image. The watermark's appearance is controlled by the blending scale. For uniformity, watermark extraction uses the same scale factor. The 3-stage DWT approach is superior because the algorithm performs well for various scaling factors that are obtained in relation to statistical characteristics connected to the Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). 2025 IEEE. -
Enhanced Edge Computing Model by using Data Combs for Big Data in Metaverse
The Metaverse is a huge project undertaken by Facebook in order to bring the world closer together and help people live out their dreams. Even handicapped can travel across the world. People can visit any place and would be safe in the comfort of their homes. Meta (Previously Facebook) plans to execute this by using a combination of AR and VR (Augmented Reality and Virtual Reality). Facebook aims to bring this technology to the people soon. However, a big factor in this idea that needs to be accounted for is the amount of data generation that will take place. Many Computer Science professors and scientists believe that the amount of data Meta is going to generate in one day would almost be equal to the amount of data Instagram/Facebook would have generated in their entire lifetime. This will push the entire data generation by at least 30%, if not more. Using traditional methods such as cloud computing might seem to become a shortcoming in the near future. This is because the servers might not be able to handle such large amounts of data. The solution to this problem should be a system that is designed specifically for handling data that is extremely large. A system that is not only secure, resilient and robust but also must be able to handle multiple requests and connections at once and yet not slow down when the number of requests increases gradually over time. In this model, a solution called the DHA (Data Hive Architecture) is provided. These DHAs are made up of multiple subunits called Data Combs and those are further broken down into data cells. These are small units of memory which can process big data extremely fast. When information is requested from a client (Example: A Data Warehouse) that is stored in multiple edges across the world, then these Data Combs rearrange the data cells within them on the basis of the requested criteria. This article aims to explain this concept of data combs and its usage in the Metaverse. 2023 IEEE. -
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Enhanced encryption technique for secure iot data transmission
Internet of things is the latest booming innovation in the current period, which lets the physical entity to process and intervene with the virtual entities. As all the entities relate to each other, it generates load of data, which lacks proper security and privacy standards. Cryptography is one of the domains of Network Security, which is one such mechanism that helps the data transmission process to be secure enough over the wireless or wired channel and along with that, it provides authenticity, confidentiality, integrity of data and prevents repudiation. In this paper, we have proposed an alternate enhanced cryptographic solution combing the characteristic of symmetric, asymmetric encryption algorithms and Public Key Server. Here, the key pairs of end points (Users Device and IoT device) are generated using Elliptic Curve Cryptography and the respective public keys are registered in Public Key Server along with their unique MAC address. Thereafter, both the ends will agree on one common private secret key, which will be the base for further cryptographic process using AES algorithm. This model can be called as multi-phase protection mechanism. It will make the process of data transmission secure enough that no intermediate can tamper the data. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Enhanced Energy Efficient Routing for Wireless Sensor Network Using Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
Recent technological advancement in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmitted to the base station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can communicate with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this paper we have proposed Extended Power Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy efficient data transmission based on PEGASIS protocol. In this proposed method average distance between the sensor nodes are considered as the criterion for chaining and fix the outermost node's radio range value the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission between the sensor node and the base station. The simulation of the proposed work shows that lifetime of the network is increased when comparing to the LEACH and PEGASIS protocol. 2021 The Authors. Published by Elsevier B.V. -
Enhanced Energy-Efficient Routing for Wireless Sensor Network Using Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol
Recent technological advancements in wireless communication and sensors made Wireless Sensor Networks (WSNs) as one of the demanding platforms in the current scenario. In WSN, tiny sensor nodes are collecting and monitoring the biological data or physical data or environmental data and transmits to the Base Station (BS) through gateway routers. These data can be accessed anywhere and anytime. Usually, sensor nodes have restrained battery power which creates the rigorous lifetime duration issues in WSN. Sensor nodes can transmit the data with each other using various routing protocols. Data transmission devours more amounts of energy and power. So, energy preservation is an important factor in WSN. There are plenty of researches going on in designing less energy consuming protocols for data transmission which helps to increase the lifetime of WSN. In this manuscript, we have proposed Extended Power-Efficient Gathering in Sensor Information Systems (E-PEGASIS) protocol for enhanced energy-efficient data transmission based on PEGASIS protocol. In this proposed method, the average distance between the sensor nodes is considered as the criterion for chaining and fixing the outermost nodes radio range value to the base station. Later it chains the related nodes available in the radio range. Consequently, the chained node checks their distance with the next nearest end node to go on with the chaining procedure which will enhance the performance of data transmission amid the base station and sensor node. The simulation of the proposed work shows that lifetime of the network is increased when compared to the LEACH and PEGASIS protocol. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Enhanced Geographical Information System Architecture for Geospatial Data
[No abstract available] -
Enhanced Horse Optimization Algorithm Based Intelligent Query Optimization in Crowdsourcing Systems
Crowdsourcing is a strategy of collecting information and knowledge from an abundant range of individuals over the Internet in order to solve cognitive or intelligence intensive challenges. Query optimization is the process of yielding an optimized query based upon the cost and latency for a given location based query. In this view, this article introduces an Enhanced Horse Optimization Algorithm based Intelligent Query Optimization in Crowdsourcing Systems (EHOA-IQOCSS) model. The presented EHOA-IQOCSS model mainly based on the enhanced version of HOA using chaotic concepts. The proposed model plans to accomplish a better trade-off between latency and cost in the query optimization process along with answer quality. The EHOA-IQOCSS is used to compute the Location-Based Services (LBS) namely K-Nearest Neighbor (KNN) and range queries, where the Space and Point of Interest (POI) can be obtained by the conviction level computation. The comparative study stated the betterment of the EHOA-IQOCSS model over recent methods. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Enhanced Image Classification using Transfer Learning with ResNet50-V2: A Case Study on Wildlife Recognition
This study explores the application of transfer learning using the ResNet50-V2 architecture for accurate classification of Arctic wildlife species, including Arctic foxes, polar bears, and walruses. Transfer learning leverages pretrained networks to enhance performance in new tasks with limited labeled data, reducing the need for extensive data collection and computational resources. In this work, we utilized a dataset of 1000 labeled images across the three species and applied ResNet50-V2, pre-trained on ImageNet, as a feature extractor. The model achieved high accuracy, with training and validation accuracies nearing 99% and 95-97%, respectively, though minor overfitting was observed. This indicates the model's strong ability to generalize across the dataset while benefiting from pre-trained weights on diverse, non-related images. Additionally it compares with models like SSD and CycleGAN, emphasizing its capability to generalize well, handle small datasets, and mitigate overfitting. We discuss model architecture, data preprocessing, and the experimental results, focusing on improvements achievable through regularization techniques to counteract overfitting. This study demonstrates the effectiveness of transfer learning for wildlife classification, providing insights into optimizing CNNs for ecological and conservation applications. 2025 IEEE. -
Enhanced image encryption using fractional-order chaotic systems and neural network-based optimization for secure multimedia applications
The rapid expansion of multimedia data in fields like healthcare and finance necessitates robust image encryption to protect sensitive content. Conventional chaotic encryption, based on integer-order systems, is hindered by restricted key spaces (e.g., and suboptimal parameter choices, exposing vulnerabilities. This work introduces an innovative encryption method that merges a fractional-order chaotic Logistic map with neural network optimization to overcome these shortcomings and enhance security. Utilizing the Grunwald-Letnikov derivative, the fractional-order Logistic map produces a complex, unpredictable sequence for encryption. A feedforward neural network fine-tunes parameters (,), elevating the Lyapunov exponent from 0.5032 to 0.6540, signifying heightened chaos. This integration harnesses fractional-order memory effects and neural network adaptability, surpassing traditional integer-order encryption constraints. The method achieves a key space of, entropy of 7.9962, and horizontal correlation of 0.0028. Parameter sensitivity tests show significant output variation with minor changes. Security analysis yields NPCR at 99.60% and UACI at 33.45%. Neural network training achieves a low mean squared error of 0.0032912 by epoch 100, with high correlation. Encryption of 256256 images in 0.21 seconds and 720p video at 41.67 fps (0.024 s/frame) supports real-time applications. By combining fractional-order chaos with machine learning, this approach delivers superior image encryption, addressing integer-order system limitations. It provides a scalable framework for secure multimedia communications. Future efforts will extend the technique to color images and video, incorporating advanced machine learning for greater resilience. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.