To ensure crop quality and productivity, almond damage detection requires efficient and reliable diagnostic techniques. This research presents an automated almond damage detection method using segmentation and deep learning-based classification. UNet is used to identify almonds in the input image properly. Deep learning architectures like VGG16, VGG19, Xception, InceptionV3, Densenet201, and NASNetMobile are used to categorize damage after segmentation. Using the cross-entropy loss function, Dice coefficient, Jaccard index, and area-under-the-curve (AUC), the performance of the proposed system is evaluated. For robust training, the segmentation models were trained on a large dataset of almond images, including damaged and undamaged examples. Evaluation metrics showed segmentation and classification accuracy and reliability. UNet+Xception leads with 98.23% accuracy. Because of its precision, deep learning can automate almond crop damage detection. With a 95.67% Dice coefficient and 92.45% Jaccard index, UNet can accurately identify damage. Precision agriculture is using more AI, enabling crop health monitoring and management research. In order to improve almond yield management and sustainability, agricultural methods must employ advanced machine-learning algorithms.

This study uses a hybrid deep learning technique to classify asphalt, pavement, and unpaved roads. In real-world circumstances, image data noise can damage image categorization algorithms. This issue can be addressed by a deep neural network (DNN)-based classification system that uses advanced denoising algorithms to improve input images before categorization. We start by denoising noisy native images with autoencoder (AE) approaches. We use two autoencoders: Denoising Autoencoder(DAE) and Convolutional Denoising Autoencoder(CDAE). Proper categorization requires models that filter noise and increase visual clarity. The CDAE employs convolutional layers to maintain spatial hierarchies and local characteristics during denoising, whereas the DAE involves encoding and decoding to rebuild images. The rebuilt images are classified using a CNN after denoising. The CNN is a preferred DNN architecture for this job since it can gather and represent complex visual input. CNN identifies classification-boosting features using noise-free image training. Experiments show this hybrid model works. With 97.92% classification accuracy, the CDAE-CNN architecture could recognize road surface types and conditions under noisy environments. This performance proves the hybrid approach's durability despite training on noise-corrupted images. It improves image classification in noisy data. Denoising algorithms improve deep learning classifier accuracy and make them more relevant in real-world applications with low image quality. These hybrid DAE-CNN/CDAE-CNN models minimize noise and properly categorize road surfaces.

AbstractSuperalloys, particularly nickel alloys such as Inconel 625, are increasingly used in biomedical engineering for manufacturing critical components such as implants and surgical instruments due to their exceptional mechanical properties and corrosion resistance. However, traditional machining methods often struggle with these materials due to their high strength and thermal conductivity. This study investigates the application of Wire Electrical Discharge Machining (WEDM) as an advanced method for processing Inconel 625 in biomedical contexts. The authors develop an Adaptive Neuro‐Fuzzy Inference System for forecasting WEDM parameters using grey‐based data. The model's variable inputs are analysed through analysis of variance (ANOVA) and Taguchi design, aiming to optimise process performance attributes relevant to biomedical applications. Comparative studies between predicted and experimental data demonstrate a high degree of accuracy, indicating that the proposed model effectively enhances the machining process. The results suggest that this intelligent system supports decision‐making in the production of high‐quality biomedical devices and components.

The paper illustrates the use of smart devices and digitization to achieve agricultural process automation and increased yield of crops. Crops need fertilizers in the form of NP-K and water in a specific quantity and time. Current process of watering and adding fertilizers to specific plants is closely studied and correlated data on growth rates and fertilizer requirement is collected. This data is integrated into the smart Fertigation system which uses multiple sensors and smart devices. This system is then used to carry out controlled drip irrigation-based fertilization and irrigation of crops. An experimental setup for the same is deployed in a lab like environment using sensors to monitor parameters such as pH level of the soil, TDS values, temperature, humidity at a given time of the day, Arduino based actuator circuit is deployed for remote operations and control systems and additionally the remote data collection/control is enabled using Python and Google APIs. This fully functional IoT fertigation system collected data as illustrated in the paper. A correlation is drawn between the sensor actual data and control parameters of fertilizer and water supplied to the soil. This paper evaluates the viability of such a system in a controlled environment and further scaling of this system for farmers benefit to autonomously manage large land parcels based on weather data and soil characteristics.

To prevent production loss, planning for and responding quickly to disease attacks on vegetable plants, such as potatoes, is essential. The leaves and stems of vegetable plants are typically the first to show signs of disease. Thanks to recent developments in deep learning algorithms, it is now possible to use leaf images for disease classification. The primary goal is to develop a deep learning-based system for forecasting and classifying crop leaf diseases. In this study, potato vegetable crops are examined. Both the training and testing data came from a publicly available source. The system was built and evaluated using Inception V3 of the convolutional neural network and the SGD, SVM, and Logistic regression classification algorithms. Therefore, the system's performance is expected to reach a new peak.

Disease attacks on vegetable plants must be anticipated and treated promptly to avoid yield loss. The majority of diseases that affect vegetable plants manifest themselves in their leaves or stems. Disease classification using leaf images is now possible due to advancements in deep learning algorithms. The primary objective is to design a system based on deep learning for the prediction and categorization of vegetable leaf disease. Corn vegetable crops are considered in this work. A publicly available dataset was used for training and testing. Convolutional neural network Inception V3 utilized to develop and test the system. As a result, the performance of the system is projected to be at its most significant level.

Most of the time reason for death is considered as due to heart disease throughout the world. Usually, heart disease cannot be predicted by medical doctors in the early stage of the disease. The modern growth in medical technologies is based on data mining; machine learning plays an essential role in predicting heat-related illness in the medical field. Healthcare data mining application is to identify risk factors associated with the onset of heart disease. In this paper, an expert system with a decision support system was proposed, which helps to identify disease-related to the heart based on knowledge of simple attributes. The accurateness and results of all classifiers are evaluated and the best classifier is selected for predicting the most accurate outcome for heart disease. This research aims to determine the possibility of heart disease and thus to take care of the heart before it is affected.

Augmented Reality interfaces have been extensively researched throughout the past few decades, with many user studies being conducted. This paper examines the landscape of research on augmented reality. We summarise the overall contribution of each field and will then present examples of influential user studies. We identify other areas of research that would be advantageous to possible future studies. There is a trend toward hands-free applications and most user testing is carried out in the laboratory. This research will also help researchers learning the best practices when conducting AR user studies.

Crop productivity is most important in the agriculture sector and is impacted by various climate and chemical factors. A huge number of losses bear by farmers just because of these two factors. The climate factors are unpredictable, and human has no control over them but the chemical factor can be controlled by automated techniques. Several solutions have been proposed by research to address such issues. But this paper is concerned to address the issue of crop recommendation based on chemical and climate conditions. In this paper, a grey wolf optimization-based deep learning approach is proposed to suggest better crops based on chemical and climate conditions. This paper considers different chemical factors such as ph, nitrogen, phosphorus and potassium and various climate factors such as rainfall, temperature, and humidity to suggest crops to farmers. The complete approach is laid down in different folds: Firstly, the high-performance Convolution neural network is used to extract important features and classify them and afterward, the grey wolf optimization is used to optimize the feature to suggest a better crop based on different factors.

Large client models use cloud computing because it has several benefits, including minimizing cost of construction resources and its elasticity property which enables services to also be up or down to current demand. And order to provide cloud services, which meet all the requirements specified in service level agreements, there are so many challenges to still be overcome from the cloud-provider viewpoint (SLAs). As data centers absorb huge quantities of power, it is a major challenge in cloud computing to increase their energy efficiency. The main purpose of this systematic analysis is to present and analyze many algorithms in this cloud computer environment used to reduce the energy from data center and to compare solutions for research challenges. In the energy-conscious cloud applications of the data center, a new combination of the Virtual Machine Image Constructor (VMIC) is possible to evaluate component efficiency. In addition, the capacity to satisfy the required SLAs would ideally reduce energy on various host machines using VM implementations. Using less VM migration and PM shutdowns than a common heuristics method. The ongoing research work on the minimizing of energy strategies used in cloud computing is a comparative research study.

Due to its volume, varied complexity, and high dynamics of data sources in health organisations, the health industry has been affected and improved from the presence of big data. Though the usage of big data analytic methods, instruments, and digital platforms is applied in a variety of fields, they have promising research guidelines for medical organisations to implement and deliver new cases of use for potential health applications. As evidenced by pioneering research initiatives, the success of medical applications in big data is dependent solely on the architecture and on the deployment of related tools. New research has been undertaken to derive specific healthcare frameworks, providing diverse analytical data capabilities for handling data sources, from electronic records to medicinal images. We have presented several analytical avenues from a variety of stakeholders in the patient-centred healthcare system. About underlying data sources and the analysis capabilities and application areas, we also reviewed different big data frameworks. Moreover, the involvement of big data instruments in the development of the health ecosystem is also presented.