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Diabetes, characterized by heightened blood sugar levels, can lead to a condition called Diabetic Retinopathy (DR), which adversely impacts the eyes due to elevated blood sugar affecting the retinal blood vessels. The most common cause of blindness in diabetics is thought to be Diabetic Retinopathy (DR), particularly in working-age individuals living in poor nations. People with type 1 or type 2 diabetes may develop this illness, and the risk rises with the length of diabetes and inadequate blood sugar management. There are limits to traditional approaches for the early identification of diabetic retinopathy (DR). In order to diagnose diabetic retinopathy, a model based on Convolutional neural network (CNN) is used in a unique way in this research. The suggested model uses a number of deep learning (DL) models, such as VGG19, Resnet50, and InceptionV3, to extract features. After concatenation, these characteristics are sent through the CNN algorithm for classification. By combining the advantages of several models, ensemble approaches can be effective tools for detecting diabetic retinopathy and increase overall performance and resilience. Classification and image recognition are just a few of the tasks that may be accomplished with ensemble approaches like combination of VGG19,Inception V3 and Resnet 50 to achieve high accuracy. The proposed model is evaluated using a publicly accessible collection of fundus images.VGG19, ResNet50, and InceptionV3 differ in their neural network architectures, feature extraction capabilities, object detection methods, and approaches to retinal delineation. VGG19 may excel in capturing fine details, ResNet50 in recognizing complex patterns, and InceptionV3 in efficiently capturing multi-scale features. Their combined use in an ensemble approach can provide a comprehensive analysis of retinal images, aiding in the delineation of retinal regions and identification of abnormalities associated with diabetic retinopathy. For instance, micro aneurysms, the earliest signs of DR, often require precise detection of subtle vascular abnormalities. VGG19′s proficiency in capturing fine details allows for the identification of these minute changes in retinal morphology. On the other hand, ResNet50′s strength lies in recognizing intricate patterns, making it effective in detecting neoneovascularization and complex haemorrhagic lesions. Meanwhile, InceptionV3′s multi-scale feature extraction enables comprehensive analysis, crucial for assessing macular oedema and ischaemic changes across different retinal layers.

The growth of cyber threats demands a robust and adaptive intrusion detection system (IDS) capable of effectively recognizing malicious activities from network traffic. However, the existing imbalance of class in network data possesses a significant challenge to traditional IDS. To overcome these challenges, this project proposes a novel hybrid Intrusion Detection System using machine learning algorithms, which includes XGBoost, Long Short-Term Memory (LSTM), Mini-VGGNet, and AlexNet, which is used to handle the unbalanced network traffic data. Furthermore, the Random Forest Regressor is used to ascertain the importance of features for enhancing detection accuracy and interpretability. Addressing the inherent class imbalance in network data is crucial for ensuring the IDS’s effectiveness. The proposed system employs a combination of oversampling techniques for minority classes and under sampling techniques for majority classes during data preprocessing. This balanced representation of network traffic data helps prevent the IDS from being biased towards the majority class and improves its ability to detect rare or novel intrusions. The utilization of Random Forest Regressor for feature extraction serves a dual purpose. It helps identify the most relevant features within the network traffic data that contribute significantly to detecting intrusions. It enables the system to prioritize and focus on these important features during model training, thereby enhancing detection accuracy while reducing computational complexity. This research contributes to the ongoing efforts to mitigate cyber threats and safeguard critical network infrastructures.

Vizhiyan, a well-established writer in child literature has been writing continuously for the welfare of the children's community. He also gives equal importance to girl children. This is one of the most precious values added in his writings. By reading Vizhiyan’s stories the self-confidence level of the children will go to a maximum level. In this way, if a child gives his/her continuous input as evinced in the works of Vizhiyan, the child will attain tremendous changes in his life which may lead to a glorious future. Hence, the purpose of the article is to explore the fruitful results of Vizhiyan’s story among female children.

The consumption of electrical energy has greatly increased lately, and the requirement of portable charge storage devices has also increased. To fulfill this, a versatile rare earth element CeO2 in nanoparticle form is selected and synthesized through a cost effective precipitation method for supercapacitor application. To improve its efficiency, 2% and 4% of transition metal chromium were doped into the ceria lattice, and their electrochemical properties were studied. The cubic fluorite structure with an average crystallite size of all the samples was 16 nm, which was confirmed through x-ray diffraction analysis. Peculiar rice shaped clusters are observed for 2% Cr doped samples through field emission scanning electron microscopy analysis. The N2 adsorption desorption studies were performed to calculate the surface area of the prepared samples. A maximum specific capacitance of 42.6 F g−1 was obtained for the 2% Cr doped CeO2 sample at 5 mV s−1 in 2M KOH in cyclic voltammetric studies. The galvanostatic charge–discharge and electrochemical impedance spectroscopy measurements were also taken to analyze the charge–discharge cycles and stability of the samples.

Although research on the preparation of metal oxides and other materials for various applications increases exponentially, it is more important to understand the need for eco-friendly methods of preparation to preserve the environment. Most of the methods available today are expensive, environmentally harmful, and inefficient with respect to mass production. The present review has explored the Rheological Phase Reaction (RPR) method, which has been extensively utilized as an eco-friendly industrial method for the preparation of metal oxides and metal oxide/carbon composite for lithium ion battery applications. Based on the literature reports, this review has two motivations: to identify the Rheological Phase Reaction (RPR) as the mass production method for preparing metal oxides, metal oxide/carbon composites, and other materials for different applications, to discuss the preparation steps involved, its advantages, the drawbacks associated; and to give a detailed review of the electrochemical performance of different metal oxides by the RPR method for application on the lithium ion battery, with particular emphasis on lithium trivanadate (LiV3O8).

Jaundice is a common neonatal condition that causes yellowing of the eyes and skin due to elevated pigment levels. Although several underlying disorders may contribute, the condition typically results from the immature liver function in newborns., Approximately 60% of babies experienced jaundice, leading to yellowing of the skin and eyes over the last five years. Due to improved screening and management procedures, the number of severe jaundice cases requiring treatment has decreased by about 20%. This condition generally resolves as the baby's liver matures and becomes more efficient at metabolizing the pigment. The "Golden Hue Detector," a non-invasive device specifically designed to detect neonatal jaundice, was developed and validated in this study. Early and accurate diagnosis of jaundice is crucial to prevent complications such as kernicterus. Traditional detection methods rely on blood tests, which can be stressful for infants and resource-intensive for healthcare providers. These methods typically require multiple blood draws, increasing the risk of infection and causing significant discomfort for newborns. [ABSTRACT FROM AUTHOR]

The image processing plays a significant role in the early detection and diagnosis of thyroid cancer. Many researchers are trying to develop and implement image-processing models for cancer detection. This article reviews and discusses the literature on image processing and thyroid cancer. The bibliometric and structured literature review methods assess the existing literature. The study indicates that the prevalence of thyroid cancer has been increasing in recent years. Numerous factors, such as raised detection through screening programs, changes in investigative criteria, and environmental and lifestyle aspects, have contributed to this trend. Furthermore, the literature finds various diagnostic and treatment methods available for patients with thyroid cancer, as well as the difficulties associated with overdiagnosis and overtreatment. Furthermore, the role of image processing techniques in the analysis and diagnosis of thyroid cancer focuses on their possible advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Smart agriculture is a rapidly emerging field that leverages the power of machine learning (ML) and artificial intelligence (AI) to enhance efficiency, sustainability and productivity in the agricultural sector. ML and AI algorithms are employed to analyze vast amounts of data collected from various sources. These technologies provide valuable insights into crop growth, soil conditions, and environmental factors, enabling farmers to detect early signs of crop diseases, predict yield outcomes, and optimize fertilizer and water usage. Additionally, virtual assistants powered by ML and AI can offer personalized recommendations on crop management practices, helping farmers enhance their decision-making process and minimize risks. [ABSTRACT FROM AUTHOR]

Aim: In this, in vitro study's objective is to assess the shear bond strength (SBS) of metal and ceramic orthodontic brackets that have been adhered to enamel using a conventional acid etch/primer and self-etching primer (SEP) adhesive method. Materials and Methods: One hundred excised human premolars were bonded on brackets and were divided into group 1 -Metal brackets were bonded on teeth and group 2 -Ceramic brackets were bonded on teeth. These two groups were further subdivided: subgroup 1a - 25 metal brackets bonded using conventional acid etch/primer adhesive system, subgroup 1b - 25 metal brackets bonded using SEP, subgroup 2a - 25 ceramic brackets bonded using conventional acid etch/primer adhesive system, and subgroup 2b - 25 ceramic brackets bonded using SEP; brackets were debonded using a computerized Instron universal testing machine. Results: The mean value of SBS of metal and ceramic brackets bonded using conventional acid etch/primer adhesive systems had higher SBS than those bonded using SEP. SBS ranged from 7.95 to 12.24 MPa which was in the acceptable clinical range. Conclusion: SBS values of all four subgroups exceeded the range of 6-8 MPa observed to be acceptable for routine clinical use. [ABSTRACT FROM AUTHOR]