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Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks
Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms. 2023 IEEE. -
Enhanced Spam Detection in Short Message Service using Hybrid Techniques
Receiving unwanted text messages, or SMS spam, costs consumers time and money and poses a security concern. To address this issue, we can deploy a system that recognizes and automatically filters out undesirable messages. This method, a testament to the advancement in technology, employs machine learning algorithms that gain knowledge from a pool of communications classified as spam or not. Managing various message contents and languages is one of the system's unique challenges. Notwithstanding these challenges, the approach may be effective in reducing unsolicited communications, improving the security of people's mobile devices and saving them time and money. To address this issue, a variety of machine learning approaches have been employed, ranging from more modern deep learning methods like Convolutional Neural Networks (CNNs) to more traditional ones like Naive Bayes. It is common practice to assess the effectiveness of SMS spam classifiers using measures like as F1-score, precision, and recall. All things considered, SMS spam classification is crucial for protecting the security and privacy of mobile phone users and has useful applications in everyday situations. Grenze Scientific Society, 2025. -
Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble
Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Enhanced supercapacitors and LPG sensing performance of reduced graphene oxide/cobalt chromate pigments for energy storage applications
It is imperative that an initial inquiry be conducted as soon as possible since the production of monolayer of carbon atoms (rGO) composites is the root cause of their poor performance in supercapacitor and LPG sensors. Here, an effort is undertaken to construct a cobalt chromate pigments-reduced graphene oxide (CoCr2O4/rGO) by solution combustion method for the supercapacitor and LPG sensor. The proposed method is efficient and easy in terms of its application to the production of CoCr2O4/rGO polycrystalline composite on a wide scale. Within the scope of this work is an investigation into the improved supercapacitor and LPG sensing behaviour of CoCr2O4/rGO polycrystalline composite. We have implemented a simple method that has been identified for mass-producing reduced graphene oxide. The Solution combustion technique was used, and it was successful in achieving this goal for the very first time. X-ray diffraction technique is used analyse crystallinity, phase, and structural investigation. The nature of gas sensing behaviour with a step function of LPG gas at 500 ppb was studied at room temperature for rGO, The CoCr2O4 pigments and 0.5CoCr2O4+0.5rGo polycrystalline composite samples. The gas response is maximum for 0.5CoCr2O4+0.5rGo polycrystalline composite in the order of 97% in compare with the reduced graphene oxide sample which shows the lowest sensitivity in the order of 26% on exposure of liquified petroleum gas (LPG). The recorded response and recovery times of 0.5CoCr2O4+0.5rGo polycrystalline composite is found to be 40 s and 52 s respectively in comparison to the rGO sample about 58 and 74 s respectively. By adding rGo to the material, the cyclic voltammetry (CV) findings demonstrate improved current density and area of CV loop with increased scan rate. In three-electrode reveals the system, a CoCr2O4-rGo material exhibits a specific capacitance of 226 F/g. Thus, the results reveals that rGo is contributing significantly to the enhancement of a supercapacitor's performance of CoCr2O4. 2023 Elsevier Ltd and Techna Group S.r.l. -
Enhanced technique for detection and prevention of phishing on websites
Phishing is a kind of assault where cyber criminals trap individuals to gain access to someone's private data like credit card details, passwords, account details, etc. The false e-mails look shockingly genuine and even the Web pages where clients are requested to enter their data may look legitimate. Forgery of a website is a sort of online assault where the phishing person builds a duplicate of a true authorized site, with the objective of misguiding a client by fishing out data that could be utilized to dupe or instigate different assaults upon the victim. In this paper, a new technique is developed using the combination of CORS, Public Repository technique and Heuristic functions. This technique allows only authorized Domain to replicate the original website. Copyright 2019 American Scientific Publishers All rights reserved. -
Enhanced transport, dielectric and magnetic properties of Ni-doped (YFeO3)0.5(BaTiO3)0.5 perovskite for NTC thermistor and multifunctional applications
The solid-state reaction method was successfully employed to synthesize the environmentally friendly polycrystalline perovskite (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5. X-ray diffraction (XRD) analysis, complemented by Rietveld refinement, confirms its multiphase crystalline structure, comprising two cubic and one orthorhombic phase. Field-emission scanning electron microscopy (FE-SEM) reveals a well-defined surface morphology, while energy-dispersive spectroscopy (EDS) and elemental mapping validate the homogeneous distribution of constituent elements. Raman and FTIR spectroscopy further confirm the vibrational and atomic structural integrity of the material. Dielectric studies indicate a high dielectric constant (?338 at 100 Hz, room temperature), with strong frequency and temperature-dependent relaxation effects. Impedance spectroscopy reveals non-Debye relaxation behaviour, NTCR characteristics and impedance in the megaohm range at lower temperatures. AC conductivity results align well with Jonscher's power law. The thermistor coefficient (?) reaches 4778.61 at 450 C, demonstrating excellent potential for thermistor applications. Magnetic studies confirm a prominent ferromagnetic response at room temperature, with a saturation magnetization of 3.654 emu g?1 and coercive field of 196.4 Oe. These combined properties make (Y0.5Ni0.5FeO3)0.5(BaTiO3)0.5 a promising candidate for multifunctional applications. 2025 RSC. -
Enhanced visible light harvesting in dye-sensitized solar cells through incorporation of solution-processable silver plasmons and anthracite-derived graphene quantum dots
The major setback for the enhanced performance of DSSC is the narrow absorption window and the interfacial exciton recombination. Therefore, in this work, the photovoltaic performance of dye-sensitized solar cells has been improved by the synergistic effect of anthracite-derived graphene quantum dots and silver plasmons. GQD and Ag coupled photoanodes were fabricated by a facile solution processable process under room temperature. The as-fabricated DSSC TiO2/Ag/GQD (TAG) exhibited an enhanced power conversion efficiency of 10.5 % with a current density of 22.40 mAcm?2 measured under solar irradiation of 100 mWcm?2 with AM 1.5G. An enhancement surpassing 30.5 % was obtained for the champion cell when compared to the pristine TiO2 based DSSC. Furthermore, this study emphasizes developing a cutting-edge approach for the high-quality use of fossil fuel-derived graphene quantum dots in energy conversion systems, thereby encouraging the green conversion of fossil fuels and broadening the potential of anthracite coal's utilization in energy conversion applications. 2024 Elsevier Ltd -
Enhanced visible light induced dye degradation and antibacterial activities of ZnO/NiO nanocomposite synthesized using Clitoria ternatea flower extract
In this study, ZnO/NiO Nanocomposites (NCs) were prepared using a rapid, simple and eco-friendly green synthesis method using medicinal flower extract of Clitoria ternatea and their visible light assisted dye degradation and antibacterial properties were investigated. The synthesised ZnO/NiO NCs were characterised by ultravioletvisible (UVVis) spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), High resolution transmission electron microscopy (TEM) and Selected area electron diffraction (SAED) studies. XRD results revealed that ZnO/NiO NCs exhibit hexagonal wurtzite and cubic crystal structure with an average crystallite size of 18 nm. HRTEM images revealed roughly spherical and hexagonal morphology with an average particle size of 23 nm. The antibacterial activity of ZnO/NiO NCs examined against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) bacteria using well diffusion method indicated significant antibacterial activity. The photocatalytic activity of the ZnO/NiO NCs showed 83.4 % and 84.4 % of dye degradation efficiency, respectively against Bromophenol Blue (BPB) and Crystal Violet (CV) dye for 150 min under sun light irradiation. The result shows that the ZnO/NiO NCs investigated in this study exhibited a strong potential agent and was successful in the removal of dye from wastewater. 2022 Elsevier B.V. -
Enhanced Wavelet Block Shrinkage Technique For Mammogram Denoising Using K-Means Clustering And Neural Networks
The proposed research study introduces a novel approach for denoising digital mammograms by improving the existing wavelet block shrinkage filtering method with K-Means clustering and a convolutional neural network. This approach involves decomposing both the original and noisy mammograms into frequency subbands using 2D discrete wavelet transformation. The resulting subbands are then grouped into multiple clusters based on similar features of the wavelet coefficients, employing K-Means clustering. This represents an improvement over the traditional block shrinkage method, which uses fixed-size blocks. These clusters from the original and noisy mammograms are paired to train a convolutional neural network, which serves as an optimal shrinkage function. This neural network-based thresholding mechanism replaces traditional hard and soft thresholding methods that rely on a universal threshold. Test results demonstrate that the proposed enhanced wavelet block shrinkage mechanism achieves a 20% improvement in peak signal-to-noise ratio and a 5% increase in structural similarity index score compared to traditional wavelet block shrinkage. Authors. -
Enhancement in air-cooling of lithium-ion battery packs using tapered airflow duct
Temperature uniformity and peak-temperature reduction of lithium-ion battery packs are critical for adequate battery performance, cycle life, and safety. In air-cooled battery packs that use conventional rectangular ducts for airflow, the insufficient cooling of cells near the duct outlet leads to temperature nonuniformity and a rise in peak temperature. This study proposes a simple method of using a converging, tapered airflow duct to attain temperature uniformity and reduce peak temperature in air-cooled lithium-ion battery packs. The conjugate forced convection heat transfer from the battery pack was investigated using computational fluid dynamics, and the computational model was validated using experimental results for a limiting case. The proposed converging taper provided to the airflow duct reduced the peak temperature rise and improved the temperature uniformity of the batteries. For the conventional duct, the boundary layer development and the increase in air temperature downstream resulted in hotspots on cells near the outlet. In contrast, for the proposed tapered duct, the flow velocity increased downstream, resulting in improved heat dissipation from the cells near the outlet. Furthermore, the study investigated the effects of taper angle, inlet velocity, and heat generation rate on the flow and thermal fields. Notably, with the increase in taper angle, owing to the increase in turbulent heat transfer near the exit, the location of peak temperature shifted from the exit region to the central region of the battery pack. The taper-induced improvement in cooling was evident over the entire range of inlet velocities and heat generation rates investigated in the study. The peak temperature rise and maximum temperature difference of the battery pack were reduced by up to 20% and 19%, respectively. The proposed method, being effective and simple, could find its application in the cooling arrangements for battery packs in electric vehicles. 2023 Y?ld?z Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). All Rights Reserved. -
Enhancement of Accuracy Level in Parking Space Identification by using Machine Learning Algorithms
Parking space identification is a crucial component in the development of intelligent transportation systems and smart cities. Accurate detection of parking spaces in urban areas can significantly improve traffic management, reduce congestion, and enhance overall parking efficiency. This proposed model is focuses on enhancing the accuracy of parking space identification through the utilization of Support Vector Machine (SVM) algorithms. The proposed methodology involves the following steps. First, a dataset comprising labelled parking space images is collected and pre-processed to ensure optimal quality and consistency. Next, feature extraction techniques are applied to capture certain relevant spatial and textural information from the images in the dataset, enabling the creation of informative feature vectors. These feature vectors are then utilized to train a SVM model, which is well-known for its capability to handle complex classification tasks. To measure the effectiveness of the SVM-based approach, a comprehensive set of experiments is carried out using real-world parking data. The performance metrics is to analysis accuracy level of the parking space identification. Comparative analysis has been done by comparing the proposed SVM approach with other popular machine learning algorithmsto demonstrate the superiority. The results indicate that the SVM-based model achieves a significantly higher accuracy level in parking space identification compared to other existing algorithms. 2023 IEEE. -
Enhancement of Agriculture Feeder Performance by Optimal Sizing and Placing of Solar PV Tree through AEO-Based Optimization Technique
Electrical demand, which makes up a large share of the overall power market, agriculture at the top of the list of priorities. To provide end users with a dependable and high-quality supply via various feeders and renewable energy sources, distribution generations are now being developed. In recent years, solar PV systems have been used to meet the demands of numerous applications, including boosting the efficiency of distribution networks. This paper presents the system with effect ive optimization method like Artificial Eco-System based Optimization Technique for identification of the best location to install distribution generation and the optimum size to minimize feeder losses. To meet service expectations, the integration of a solar PV system is swapped out for a solar tree in this suggested work. A 28-bus Indian agriculture feeder is considered for better understanding the proposed algorithm. MATLAB software is used for implementing the proposed optimization technique and CREO-2.0 is used for designing the 3-dimensional solar PV tree. 2023 by the Kamal Kumar U and Varaprasad Janamala. -
Enhancement of coal nanostructure and investigation of its novel properties
Coal is a mineral and is extensively used as a solid fuel in developing nations and has a sizeable share in the global fossil fuel reserve. Utilization of this resource generates excess spoil and large volume of low grade waste to the environment. In recent years there have been serious research on enhancing its value and exploring the utility of this carbonaceous material to novel carbon materials. The Minerals, Metals & Materials Society 2018. -
Enhancement of efficiency of military cloud computing using lanchester model
Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher. 2020 IEEE. -
Enhancement of free convection from horizontal-base straight-fin heat sink by partial shrouding
This work presents a simple method to improve natural convection heat transfer performance of horizontal-base straight-fin heat sink by adding partial shroud plates on top of the heat sink at both ends. Experiments are conducted in conjunction with a detailed three-dimensional (3D) computational study. The numerical model is validated using experimental results. With partial shrouding, the modification and effective utilization of airflow surrounding the heat sink leads to significant heat transfer enhancement. The installation of shroud plates effectively improves the mass flowrate of air admitted into the fin channel. Further, the airflow drawn above the heat sink dissipates heat from the upper surface of the shroud plate. There is also a significant heat dissipation from the lower surface of the shroud plate which is exposed to cold air drawn from the side-end of the heat sink. The heat transfer from the existing optimal conventional heat sink is improved by 17% with the introduction of shroud plates. An optimal width of the shroud plate is identified to exist for the maximum heat transfer. The percentage enhancement in heat transfer achieved by partial shrouding increases with a decrease in the fin height and with an increase in the fin spacing. The proposed compact heat sink design would be of application in enhancing passive heat dissipation from light-emitting diode (LED) lights and other electronic devices, especially when size constraints exist. Copyright 2020 by ASME -
Enhancement of Image Classification Using Transfer Learning and GAN-Based Synthetic Data Augmentation
Plastic bottle recycling has a crucial role in environmental degradation and protection. Position and background should be the same to classify plastic bottles on a conveyor belt. The manual detection of plastic bottles is time consuming and leads to human error. Hence, the automatic classification of plastic bottles using deep learning techniques can assist with the more accurate results and reduce cost. To achieve a considerably good result using the DL model, we need a large volume of data to train. We propose a GAN-based model to generate synthetic images similar to the original. To improve the image synthesis quality with less training time and decrease the chances of mode collapse, we propose a modified lightweight-GAN model, which consists of a generator and a discriminator with an auto-encoding feature to capture essential parts of the input image and to encourage the generator to produce a wide range of real data. Then a newly designed weighted average ensemble model based on two pre-trained models, inceptionV3 and xception, to classify transparent plastic bottles obtains an improved classification accuracy of 99.06%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Enhancement of mechanical properties of epoxy/halloysite nanotube (HNT) nanocomposites
The particulate filled epoxy composites show lower mechanical properties than neat composites due to lack strength of uniform dispersion of particles leading to poor in interfacial strength between matrix and fillers. In this study, ultrasonification dispersion technique is employed to achieve a homogenous dispersion of halloysite nanotubes (HNTs) in epoxy resin. The nanocomposites are fabricated by solution casting method and specimens are prepared as per ASTM standards. The various test has been conducted as per ASTM procedure to evaluate the mechanical properties viz. density, hardness, tensile, flexural, ILSS and impact strength of the nanocomposites consisting of different weight (wt)% of HNTs varying in the range of 04 with the interval of 1. As per the experimental investigation, the selected dispersion techniques enhances the mechanical properties of the nanocomposite by the loading of HNT. Further, the study revealed that the 3wt% of HNT with ultrasonic homogenized nanocomposite shows superior mechanical strength as compared to other nanocomposites. Hence it is evident that the properties of the nanocomposite depends on the quantity of filler present and dispersion condition. The dispersion condition and fractured surfaces are analyzed through the SEM micrographs. 2019, Springer Nature Switzerland AG. -
Enhancement of nitrogen assimilation and photosynthetic efficiency by novel iron pulsing technique in Oryza sativa L. var Pankaj
Rice is a major food crop. Due to urbanization and climate change, rice production is declining, posing a threat to the increasing food demand. For this, a modified technique of priming is used to enhance plant vigor. In the present study an endogenous rice cultivar was treated with two different iron salts for 72 h and grown for 14 days in nutrient solution. This increased the iron content of the samples which further escalated the photosynthetic efficiency and carbon assimilation in the treated plants. Photosynthesis being correlated to nitrogen assimilation, nitrogen assimilation intermediates and protein content were also elevated in treated plants. Plants showed no symptoms of stress as evident from low malondialdehyde content and increased antioxidant enzymes activity. From this study it can be inferred that, treatment with iron during germination, helps to trigger growth by facilitating photosynthesis and nitrogen assimilation. 2019 Elsevier Masson SAS -
Enhancement of Phenolic and Polyacetylene Accumulation in Lobelia chinensis (Chinese lobelia) Plantlet Cultures Through Yeast Extract and Salicylic Acid Elicitation
Lobelia chinensis (Lour.) is a medicinal plant that contains phytochemicals, such as phenolics and polyacetylene compounds, with beneficial biological activities. In vitro cultures are typically employed for biomass generation and plant multiplication. However, the current biotechnological approaches for producing these chemicals are ineffective, which is why bioelicitors are being used to boost synthesis of these molecules. Plantlet cultures were established in vitro using Murashige and Skoog medium supplemented with 3% sucrose (w/v). Following 4 weeks of culture initiation, the plantlet cultures were treated with 0, 25, 50, 100, or 200 mg L?1 of yeast extract (YE) or with 0, 25, 50, 100, or 200 M of salicylic acid (SA) for 1 week to boost the synthesis of bioactive compounds. The amounts of total phenolics, total flavonoids, specific phenolics including catechin, phloretic acid, linarin, and polyacetylenes, including lobetyolinin and lobetylin, were considerably elevated in the plantlet cultures treated with 50 mg L?1 YE and/or 25 M SA. The 2,2 Diphenyl 1 picrylhydrazyl (DPPH) radical scavenging assay, 2,2?-azino-bis (3-ethybenzothiazoline-6-sulphonic acid) (ABTS) assay, and ferric reducing antioxidant power (FRAP) assay were performed to assess the antioxidant properties of the plantlets. The elicitor-treated plantlets were found to have higher antioxidant activity. Thus, plantlet biomass produced in vitro can be used as a raw material to produce medicinal and nutraceutical products. 2025 by the authors. -
Enhancement of Phenolic and Polyacetylene Production in Chinese Lobelia (Lobelia chinensis Lour.) Plant Suspension Culture by Employing Silver, Iron Oxide Nanoparticles and Multiwalled Carbon Nanotubes as Elicitors
Silver nanoparticles (AgNPs), iron oxide nanoparticles (Fe2O4NPs), and multiwalled carbon nanotubes (MWCNTs) are widely used in various applications, such as biomedicine, environmental remediation, and agriculture. In addition, these nanomaterials can affect the production of bioactive compounds in plants that have pharmacological activities. In the current study, the in vitro plant cultures of Chinese lobelia (Lobelia chinensis Lour.) were established in MS medium and treated with 0, 12.5, 25, 37.5, and 50 mg L?1 AgNPs or Fe2O4NPs, or MWCNTs. Initially, plants were grown for four weeks without any elicitors, and after that, the cultures were treated with nano-elicitors for one week. After five weeks, the effects of nano-elicitors were estimated on growth, total phenolic, flavonoids, polyacetylenes, and ABTS/DPPH/FRAP antioxidant activity was investigated. The results showed that lower levels of AgNPs (25 mg L?1), Fe2O4NPs (25 mg L?1), and MWCNTs (12.5 mg L?1) favored the accumulation of fresh and dry biomass. Whereas, 37.5 mg L?1 AgNPs, 25 mg L?1 Fe2O4NPs, and 37.5 mg L?1 MWCNTs enhanced the accumulation of total phenolics, flavonoids, specific phenolic compounds including chlorogenic acid, catechin, phloretic acid, coumaric acid, salicylic acid, naringin, myricetin, linarin, and polyacetylenes viz. lobetylonin and lobetyolin in higher concentrations. The plant extracts elicited by nanomaterials also depicted very good antioxidant activities according to ABTS, DPPH, and FRAP assays. These results suggest that specific nanomaterials, and at specific levels, could be used for the production of bioactive compounds from shoot cultures of Chinese lobelia. 2025 by the authors.