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Enhanced Jaya Optimization Algorithm with Deep Learning Assisted Oral Cancer Diagnosis on IoT Healthcare Systems
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Enhanced Learning in IoT-Based Intelligent Plant Irrigation System for Optimal Growth and Water Management
This research looked at the transformative potential of cutting-edge machine learning algorithms in various areas of precision agriculture, with an emphasis on enhancing smart irrigation systems for onion farming. Using a vast sensor network and real-time monitoring, we investigated the performance of CNN, ANN, and SVM, three well-known machine learning algorithms. After extensive testing and investigation, our results reveal that CNN beats ANN and SVM in terms of outstanding accuracy in predicting plant water requirements. Because of CNN's superior predictive powers, our intelligent irrigation system maintains perfect soil conditions, resulting in increased agricultural yields and resource savings. The study's findings have important implications for modern agriculture, paving the way for data-driven, sustainable agricultural methods that address global concerns such as food security and environmental sustainability. As we approach the era of smart agriculture, our research demonstrates how technology has the potential to alter crop farming and aid in the development of a more resilient and successful agricultural industry. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Enhanced Level Brain Tumor Identification Using CNN, VGG16 and ResNet Models
The comprehension of brain growths is significantly improved through the identification and categorization of these disorders. Still, their discovery is relatively grueling due to their variability in terms of position, shape, and size. Fortunately, deep literacy has revolutionized the field and significantly improved recognition, prediction, and opinion in various healthcare areas, including brain excrescences. The main goal of this study is to thoroughly review exploration that utilizes CNN, VGG16, and RESNET infrastructures to classify brain excrescences using MRI images. The performance of these models varied significantly, with CNN, VGG16, and RESNET achieving an emotional delicacy of 99.6. Additionally, ResNet and VGG16 achieved rigor of 92.4 and 89.7 independently. Likewise, the visualization of the decision-making processes of these models has provided valuable insight into the features they prioritize. By incorporating these models into their practice, healthcare professionals have the opportunity to enhance their individual capabilities, eventually leading to improved patient outcomes. 2024 IEEE. -
Enhanced light harvesting in DSSCs using carbon dots derived from Alstonia venenata
This study presents a novel co-sensitization strategy utilizing carbon dots derived from Alstonia venenata in combination with the N719 dye to enhance the light-harvesting efficiency of dye-sensitized solar cells (DSSCs). The carbon dots were synthesized via a hydrothermal process using an aqueous extract of Alstonia venenata leaves, resulting in a material with broad absorption characteristics. These synthesized carbon dots were then drop-cast onto an N719-sensitized photoanode, leading to improved carrier generation and enhanced device performance. The selection of Alstonia venenata as a precursor is based on its rich phytochemical composition, which contains alkaloids, flavonoids, terpenoids and phenolic compounds that act as efficient carbon precursors and surface passivation agents. Upon carbonisation, these biomolecules yield functionally active carbon dots that can improve electron transport, minimise charge recombination and enhance dye anchoring at the TiO2 surface. Carbon dots have demonstrated significant potential as co-sensitizers, offering a highly effective approach to increasing DSSC efficiency. Their strong binding affinity further facilitates efficient photoinduced electron transfer to the photoanode, contributing to improved device functionality. In this research, TiO2 was employed as the photoanode, while N719 dye and carbon-dot-modified N719 served as sensitizers. Iodolyte HI-30 acted as the electrolyte, and Platisol T/sp functioned as the counter electrode. The unmodified DSSC exhibited a power conversion efficiency of 5.2%, which was enhanced to 6.0% with the incorporation of carbon dots as co-sensitizers. The significant efficiency improvement achieved through this co-sensitization strategy underscores the unique capabilities of carbon dots derived from Alstonia venenata, making this approach a promising advancement toward the development of cost-effective and high-performance DSSCs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Enhanced Light Scattering Using a Two-Dimensional Quasicrystal-Decorated 3D-Printed Nature-Inspired Bio-photonic Architecture
A number of strategies have been exploited so far to trap photons inside living cells to obtain high-contrast imaging. Also, launching light inside biological materials is technically challenging. Using photon confinement in a three-dimensional (3D)-printed biomimetic architecture in the presence of a localized surface plasmon resonance (LSPR) promoter can overcome some of these issues. This work compares optical confinement in natural and 3D-printed photonic architectures, namely, fish scale, in the presence of atomically thin Al70Co10Fe5Ni10Cu5 quasicrystals (QCs). Due to their wideband LSPR response, the QCs work as photon scattering hotspots. The architecture acts as an additive source of excitation for the two-dimensional (2D) QCs via total internal reflection (TIR). The computational analysis describes the surface plasmon-based scattering property of 2D QCs. The 3D-printed fish scale's image contrast with the 2D Al70Co10Fe5Ni10Cu5 QC has been compared with other 2D materials (graphene, h-BN, and MoS2) and outperforms them. The present study conceptually presents a new approach for obtaining high-quality imaging of biological imaging, even using high-energy photons. 2023 American Chemical Society. -
Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods
Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE. -
Enhanced mechanical properties of CNT/Graphene reinforced PLA-based composites fabricated via fused deposition modelling
This study addresses the mechanical properties of polylactic acid (PLA) composite materials reinforced by CNTs and graphene, produced using the Fused Deposition Modeling process. The collaborative impact of graphene and CNTs upon the primary mechanical attributes including UTS, yield strength, modulus of elasticity, and impact resistance has been investigated. Three distinct CNT's weight percentages of 0.5, 1, and 1.5 have been fabricated under constant graphene content at 0.5 wt%. These findings revealed that the UTS of pure PLA were 28 MPa, whereas for the composite with 1.5 wt% CNT and 0.5 wt% graphene, it was raised to 48 MPa. From 2.6 GPa to 4 GPa the young's modulus enhancement is seen and the yield strength enhancement is seen up to 28 MPa for the composite from 20 MPa of pure PLA. The impact strength was greatly enhanced from 1.2 J for pure PLA to as high as 4 J for the composite comprising 1.5 wt percentage CNT and 0.5 wt percentage graphene. 2025 The Author(s) -
Enhanced mother optimization algorithm-based optimal reconfiguration to accommodate emerging electric vehicle demand
Radial configuration and high x/r ratio branches in electrical distribution systems (EDSs) result in significant power losses and diminished stability margins. Optimal network reconfiguration (ONR) is a highly flexible solution methodology for addressing these challenges. The identification of optimal branches or tie lines to modify their on/off status in relation to multiple objectives under radial constraints constitutes a complex optimization challenge. This paper presents a novel variant of the mother optimization algorithm (MOA) that incorporates dynamic learning techniques for the optimal placement and sizing of electric vehicle (EV) charging stations to enhance distribution system loadability. The proposed modifications enhanced the overall performance of the algorithm by improving the exploration and exploitation characteristics. This leads to superior global best results and faster convergence than with other competitive algorithms when addressing complex optimization problems. In addition, an enhanced mother optimization algorithm (EMOA) is employed to address the ONR problem in 7-, 12-, 33-, 69-, and 118-bus IEEE radial systems. The losses are reduced by 44.15%, 30.07%, 33.87%, 55.72%, and 33.04% when compared to the base case across the respective test systems. Moreover, the loadability is increased in the 33-bus and 69-bus configurations by 208.75% and 177.07% for the base and optimal configurations, respectively. The results indicate the appropriateness of the ONR for enhancing the loadability to accommodate the rising penetration levels of electric vehicles (EVs) in support of sustainability. The Author(s) 2025. -
Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE. -
Enhanced photocatalytic activity of La3+ doped bicrystalline titania prepared via combustion method for the degradation of cationic dye under solar illumination
La3+ doped TiO2 photocatalysts were successfully synthesized by combustion method in the presence of urea and were characterized by various physico-chemical techniques. Further, the photocatalytic performance of the synthesized catalysts was monitored by photocatalytic degradation of synthetic cationic dye-Methylene Blue (MB) under solar illumination. The bicrystalline phase of anatase and rutile was confirmed by X-ray diffraction analysis. Moreover, the transformation from anatase to rutile phase proceeds at a slower rate in the La3+ doped TiO2 catalysts. Effective separation of charge carriers, a synergistic effect in the bicrystalline framework of anatase and rutile, smaller crystallite size, and higher concentration of surface adsorbed hydroxyl groups helped these catalysts to show improved activity for the dye degradation. Copyright 2018 BCREC Group. All rights reserved. -
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures
Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. 2025 Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav and Rituraj Jain. -
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G. -
Enhanced power quality control of a photo voltaic power plant integrated with multiple electric vehicle
As there is a great need for high-quality electricity on the distribution side, distribution side generation (DSG) has become increasingly important. The increased weight of EVs on the distribution side is the cause of this. There are numerous power quality mitigation techniques employed to address this type of issue, but many of the solutions suggest the usage of a separate device, such as an active power filter. But while construction the DSG the solution to this problem may be addressed using the proposed solution in this paper. Power quality (PQ) problems are being caused by the grids integration of Photo-Voltaic (PV) and its application to all connected loads. With the aid of Direct Quardrature (DQ) controller and Multicarrier Space Vector Pulse Width Modulation (SVPWM) technology, the overall power quality disturbance is decreased. A Simulink model for the PV-EV-Grid system was built to measure voltage and current Total Harmonic Distortion (THD) percentages under linear, non-linear, and Plug in Hybrid Vehicle (PHEV) load situations. The model shows that the THD values are well within the IEEE 519. Indian Academy of Sciences 2024. -
Enhanced Process Model and Analysis of Risk Integration in Software effort estimation
The development of software within the estimated effort is remaining as a challenging task. The process of effort estimation is a critical activity in a software project, where effort estimates are utilized to arrive at the schedule, resources, and cost. Though many software effort estimation techniques exist, effort overrun occurs in a project. Identification of risks and their consideration in software lifecycle activities play a significant role in the successful execution of a software project. It would be required to account for uncertainty and the key factors that contribute to it. This study focuses on the need to include project risk score in the software effort estimation process to arrive at better effort estimates. This paper depicts the standard and enhanced process frameworks for estimation of software development efforts. A multi-layer perceptron model was built and the results indicated the relevance of considering project risk score in the effort estimation process. The usage of an enhanced gradient boosting technique for predictive modelling revealed a decrease in standard deviation of the residuals, thus indicating a better fit for the effort estimation model through integration of risks. 2019 IEEE. -
Enhanced radial basis function neural network for tomato plant disease leaf image segmentation
Primary crop losses in agriculture are due to leaf diseases, which farmers cannot identify early. If the diseases are not detected early and correctly, then the farmer will have to undergo huge losses. Therefore, in the field of agriculture, the detection of leaf diseases in tomato crops plays a vital role. Recent advances in computer vision and deep learning techniques have made disease prediction easy in agriculture. Tomato crop front side leaf images are considered for research due to their high exposure to diseases. The image segmentation process assumes a significant role in identifying disease affected areas on tomato leaf images. Therefore, this paper develops an efficient tomato crop leaf disease segmentation model using an enhanced radial basis function neural network (ERBFNN). The proposed ERBFNN is enhanced using the modified sunflower optimization (MSFO) algorithm. Initially, the noise present in the images is removed by a Gaussian filter followed by CLAHE (contrast-limited adaptive histogram equalization) based on contrast enhancement and un-sharp masking. Then, color features are extracted from each leaf image and given to the segmentation stage to segment the disease portion of the input image. The performance of the proposed ERBFNN approach is estimated using different metrics such as accuracy, Jaccard coefficient (JC), Dice's coefficient (DC), precision, recall, F-Measure, sensitivity, specificity, and mean intersection over union (MIoU) and are compared with existing state-of-the-art methods of radial basis function (RBF), fuzzy c-means (FCM), and region growing (RG). The experimental results show that the proposed ERBFNN segmentation model outperformed with an accuracy of 98.92% compared to existing state-of-the-art methods like RBFNN, FCM, and RG, as well as previous research work. 2022 Elsevier B.V. -
Enhanced Random Forest-Based Model forFlood Detection andClassification
Flooding is one of the most devastating natural disasters globally, causing extensive damage to infrastructure, the environment, and human lives. With increasing occurrences due to climate change, accurate classification and analysis of flood imagery are essential for early detection, damage assessment, and post-disaster recovery. Reliable flood classification systems are critical for early warning, resource allocation, and mitigation efforts, helping to minimize the impact on affected regions. Remote sensing and computer vision techniques, including the Bag-of-Visual-Words (BOV) model, offer powerful tools for interpreting flood images by categorizing and identifying flooded regions across vast and complex terrains. This paper presents a modification of the standard Random Forest algorithm to enhance the accuracy of image classification within a Bag-of-Visual-Words (BOV) model. The modified Random Forest achieves better adaptability and performance across flood image datasets by introducing flexibility in parameter tuning through custom hyperparameters and automatic grid search. This modification addresses challenges in balancing efficiency and accuracy for classifying high-dimensional image data sets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Enhanced Secure Technique for Detecting Cyber Attacks Using Artificial Intelligence and Optimal IoT
The Internet of Things (IoT) is a broad term that refers to the collection of information about all of the items that are linked to the Internet. It supervises and controls the functions from a distance, without the need for human interaction. It has the ability to react to the environment either immediately or via its previous experiences. In a similar vein, robots may learn from their experiences in the environment that is relevant to their applications and respond appropriately without the need for human interaction. A greater number of sensors are being distributed across the environment in order to collect and evaluate the essential information. They are gaining ground in a variety of industries, ranging from the industrial environment to the smart home. Sensors are assisting in the monitoring and collection of data from all of the real-time devices that are reliant on all of the different types of fundamental necessities to the most advanced settings available. This research study was primarily concerned with increasing the efficiency of the sensing and network layers of the Internet of Things to increase cyber security. Due to the fact that sensors are resource-constrained devices, it is vital to provide a method for reacting, analysing, and transmitting data collected from the sensors to the base station as efficient as possible. Resource requirements, such as energy, computational power, and storage, vary depending on the kind of sensing devices and communication technologies that are utilised to link real-world objects together. Sensor networks' physical and media access control layers, as well as their applications in diverse geographical and temporal domains, are distinct from one another. Transmission coverage range, energy consumption, and communication technologies differ depending on the application requirements, ranging from low constraints to high resource enrich gadgets. This has a direct impact on the performance of the massive Internet of Things environment, as well as the overall network lifetime of the environment. Identifying and communicating matching items in a massively dispersed Internet of Things environment is critical in terms of spatial identification and communication. 2022 Anand Kumar et al. -
Enhanced Security for Large-Scale 6G Cloud Computing: A Novel Approach to Identity based Encryption Key Generation
Cloud computing and 6G networks are in high demand at present due to their appealing features as well as the security of data stored in the cloud. There are various challenging methods that are computationally complicated that can be used in cloud security. Identity-based encryption (IBE) is the most widely used techniques for protecting data transmitted over the cloud. To prevent a malicious attack, it is an access policy that restricts access to legible data to only authorized users. The four stages of IBE are setup, key extraction or generation, decryption and encryption. Key generation is a necessary and time-consuming phase in the creation of a security key. The creation of uncrackable and non-derivable secure keys is a difficult computational and decisional task. In order to prevent user identities from being leaked, even if an opponent or attacker manages to encrypted material or to decode the key this study presents an advanced identity-based encryption technique with an equality test. The results of the experiments demonstrate that the proposed algorithm encrypts and decrypts data faster than the efficient selective-ID secure IBE strategy, a competitive approach. The proposed method's ability to conceal the identity of the user by utilizing the Lagrange coefficient, which is constituted of a polynomial interpolation function, is one of its most significant aspects. 2023 The Authors. Published by AnaPub Publications. This is an open access article under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/) -
Enhanced Security in Payment Gateways Through Face Detection: An Advanced Approach Using DenseNet 121- BiLSTM Models
Because it is one of the most promising applications of image analysis, face recognition has been the subject of intense research and development for many decades. Many modern identification and verification requirements have found a potential new home with the introduction of face recognition (FR) technology. Facial recognition is just one of numerous uses for biometric pattern recognition algorithms. Sequencing is essential for many tasks, including as feature extraction, model training, and preprocessing. Eliminating background noise and obtaining dense vertical edges are part of the preprocessing procedures. Facial feature extraction will be employed to extract features after feature extraction. Use attributes cautiously when training a Desnet121-BiLSTM model. In every respect, the suggested method outperforms two state-of-the-art algorithms, Desnet121 and BiLSTM. An accuracy rating of 97.19% was indicative of a considerable improvement in the figures. 2024 IEEE. -
Enhanced Sensing Performance of an Ammonia Gas Sensor Based on Ag-Decorated ZnO Nanorods / Polyaniline Nanocomposite
The development of low-cost ammonia sensors with high sensitivity and selectivity has gained considerable interest. Though the response of these sensors at room temperature is low and needs enhancement. In the present study high sensitivity ammonia gas sensors based on nanocomposite films of polyaniline (PANI) and with varying ZnO concentrations were synthesized and investigated. With a loading of 10 at% ZnO, the gas sensing response of 59 % was obtained for 120 ppm NH3 gas. The gas response was further enhanced by decorating the ZnO nanorods with different concentrations of silver (Ag) nanoparticles. The Ag-decorated ZnO nanorods were embedded in the PANi matrix using the in-situ oxidative polymerization technique. It was shown that PANi ZnO, p-n junction, and the introduction of porosity in nanocomposite act synergistically in increasing the resistance caused by the deprotonation of PANi by NH3. Among various compositions studied, 2 % loading of Ag in ZnO embedded in PANi matrix, thin films were found to be highly selective and sensitive towards NH3 gas at room temperature with a chemiresistive response of 70 % at 120 ppm and a recovery time of less than 120 s. The selectivity of the nanocomposite was also studied towards various reducing and oxidizing gasses. 2023 Wiley-VCH GmbH.