Real-world applications benefit greatly from aerial imagery. AVarious modern applications utilizing aerial images; however, these images are often low-contrast due to imperfect atmospheric conditions and limitations in the imaging systems. Many methods exist to enhance the quality of aerial images, yet not all of them capable of producing desired results. Some may have high complexity, and others may require numerous inputs. On the other hand, it is observed that a low-contrast impact that is difficult to prevent throughout the data collection process degrades the quality of aerial images a lot. AsAa result, in this paper a novel method for improving aerial image contrast has been presented. Hence a two-phase approach for increasing contrast and remapping the intensities of an aerial image to its native dynamic range has been presented in this paper. Additionally, a regularization technique is provided using the two-step regularization and mapping procedures. For image quality assessment (IQA), two performance assessment metrics; measure of enhancement (EME) and Structural SIMilarity (SSIM) have been suggested to measure the quality of the results of the proposed algorithm. The experimental results indicate that the proposed approach increases the contrast of aerial image substantially as compared with other widely contrast enhancement methods.

Deep learning's rapid development is generating significant interest in its potential to improve medical imaging. It has shown promising results in detecting malignant lymphoma in histopathology medical images. Image classification methods are widely used to aid in making diagnoses from medical images. In recent years, deep learning methods have achieved high performance in detecting malignant lymphoma in histopathology images. This study proposes a novel approach to improving lymphoma diagnosis in histopathology images called the Lightweight Convolutional Neural Network (LWCNN). The proposed LWCNN model comprises multiple deep learning architectures, including a convolutional neural network (CNN) that has been trained to classify lymphoma subtypes based on histopathology images using ResNet50 and MobileNetV2. The LWCNN model aggregates the predictions of disparate architectures to arrive at a definitive diagnosis, leveraging the unique capabilities of each. A comprehensive dataset of annotated lymphoma histopathology images was assembled for the purpose of training the multi-deep learning model. To ensure a representative and diverse training set, the dataset was meticulously curated to encompass various subtypes of lymphoma. Performance evaluation of the proposed deep learning model for lymphoma classification using standard metrics revealed the following accuracies: LWCNN (97.34% training, 86.71% testing), ResNet50 (88.76% training, 86.37% testing), and MobileNetV2 (88.47% training, 86.60% testing). These results indicate that the LWCNN model significantly surpasses existing approaches in diagnostic accuracy.

For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. While the GRU layer records temporal relationships and sequence information, the CNN layer analyzes the incoming data to extract significant features. Using a publicly accessible type 1 diabetes dataset, the hybrid model’s performance is compared to that of the standalone Long Short-Term Memory (LSTM), CNN, and GRU models. The findings show that the hybrid CNN-GRU model performs better than the single models, indicating its potential to significantly improve real-time BGL forecasting in IoT-based diabetes management systems.

Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.

The demand for effective e-learning platforms requires prioritizing pedagogical excellence in online educational websites. Current approaches struggle with uncertainties, hindering optimal e-learning environments due to a lack of comprehensive evaluation in traditional methods. An integrated approach is crucial to avoid inefficiencies and incomplete understanding of learner needs. This research introduces a pioneering methodology integrating Inter-Valued Fuzzy (IVF) COPRAS-EDAS-PIV hybrid Multiple Criteria Decision-Making (MCDM) techniques, addressing existing limitations. Leveraging the IVF concept allows a holistic assessment of pedagogical parameters, ensuring a thorough understanding of the decision-making landscape. The study involves an extensive literature review, parameter identification, and data acquisition through group decision-making. The selection of a suitable e-learning website is based on seven conflicting parameters, and preference ranking orders are prescribed using EDAS, COPRAS, and PIV MCDM model. Rigorous analysis using these techniques facilitates precise ranking and informed decision-making. The findings underscore the efficacy of the proposed IVF-MCDM approach for the design of a pedagogical e-learning website. Final results reveal that alternative 5 as the most preferable, followed by alternative 2, while alternative 3 is the least favored option among the group. Comparative and sensitivity analyses validate the approach’s superiority, enabling stakeholders to make well-informed decisions for optimal e-learning websites that cater to diverse learner needs, thus enhancing the overall online learning experience.

The rapid advancement of deep learning has generated considerable enthusiasm regarding its utilization in addressing medical imaging issues. Machine learning (ML) methods can help radiologists to diagnose breast cancer (BCs) barring invasive measures. Informative hand-crafted features are essential prerequisites for traditional machine learning classifiers to achieve accurate results, which are time-consuming to extract. In this paper, our deep learning algorithm is created to precisely identify breast cancers on screening mammograms, employing a training method that effectively utilizes training datasets with either full clinical annotation or solely the cancer status of the entire image. The proposed approach utilizes Lightweight Convolutional Neural Network (LWCNN) that allows automatic extraction features in an end-to-end manner. We have tested LWCNN model in two experiments. In the first experiment, the model was tested with two cases' original and enhancement datasets 1. It achieved 95 %, 93 %, 99 % and 98 % for training and testing accuracy respectively. In the second experiment, the model has been tested with two cases' original and enhancement datasets 2. It achieved 95 %, 91 %, 99 % and 92 % for training and testing accuracy respectively. Our proposed method, which uses various convolutional network to classify screening mammograms achieved exceptional performance when compared to other methods. The findings from these experiments clearly indicate that automatic deep learning techniques can be trained effectively to attain remarkable accuracy across a wide range of mammography datasets. This holds significant promise for improving clinical tools and reducing both false positive and false negative outcomes in screening mammography.

Melanoma skin cancer is primarily characterized by poor prognostic responses. Surgical treatment can achieve advanced cure rate with early melanoma detection. Manual segmentation of suspected lesions aids early melanoma diagnosis. However, the limitations of manual segmentation include low efficiency and a risk of misclassification. Deep learning, due to its proficiency in image object classification, has gained popularity and is usually used in medical specialties such as ophthalmology, dermatology, and radiology. This paper proposes a deep learning method using a novel light weight convolutional neural networks (LWCNN) and transfer learning techniques (GoogleNet, ResNet-18 & MobilNetv2). These are used to train datasets and features enhancement of skin scan gathered from Kaggle, aiming to distinguish them into two groups: Melanoma and Non-Melanoma cells. By employing these techniques, new datasets with robust features are produced. All CNN models have been tested in two experiments. In firestone, model was tested solely with original datasets and achieved 97.30%, 88.43%, and 48.28% for AC-Training, AC-Testing, and Time (min) respectively. In second experiment, we used the dataset after enhancing the features of skin scan images, which resulted in 99.18%, 91.05%, and 22.54% for ACTraining, AC-Testing, and Time (min) respectively. According to experimental results, the proposed approach provides higher accuracy results for enhanced images than original images, demonstrating its potential in skin cancer classification.

Sentiment analysis also referred to as opinion mining, plays a significant role in automating the identification of negative, positive, or neutral sentiments expressed in textual data. The proliferation of social networks, review sites, and blogs has rendered these platforms valuable resources for mining opinions. Sentiment analysis finds applications in various domains and languages, including English and Arabic. However, Arabic presents unique challenges due to its complex morphology characterized by inflectional and derivation patterns. To effectively analyze sentiment in Arabic text, sentiment analysis techniques must account for this intricacy. This paper proposes a model designed using the transformer model and deep learning (DL) techniques. The word embedding is represented by Transformer-based Model for Arabic Language Understanding (ArabBert), and then passed to the AraBERT model. The output of AraBERT is subsequently fed into a Long Short-Term Memory (LSTM) model, followed by feedforward neural networks and an output layer. AraBERT is used to capture rich contextual information and LSTM to enhance sequence modeling and retain long-term dependencies within the text data. We compared the proposed model with machine learning (ML) algorithms and DL algorithms, as well as different vectorization techniques: term frequency-inverse document frequency (TF-IDF), ArabBert, Continuous Bag-of-Words (CBOW), and skipGrams using four Arabic benchmark datasets. Through extensive experimentation and evaluation of Arabic sentiment analysis datasets, we showcase the effectiveness of our approach. The results underscore significant improvements in sentiment analysis accuracy, highlighting the potential of leveraging transformer models for Arabic Sentiment Analysis. The outcomes of this research contribute to advancing Arabic sentiment analysis, enabling more accurate and reliable sentiment analysis in Arabic text. The findings reveal that the proposed framework exhibits exceptional performance in sentiment classification, achieving an impressive accuracy rate of over 97%.

The segmentation of liver images from computed tomography (CT) scans is a pivotal technique that supports various medical applications, including computer -aided diagnostics, disease identification, and the evaluation of hepatic function. In this study, an advanced segmentation method for CT liver images is introduced, leveraging the synergy between Renyi entropy and fuzzy c -partition methodologies. The proposed approach commences with the enhancement of input CT images employing an adaptive histogram equalization technique, thereby improving the contrast of hepatic tissues. Subsequently, these images are transformed into the fuzzy domain, wherein the entropies of the hepatic object and the surrounding tissue are meticulously defined. The optimization of the Renyi entropy measure is adeptly carried out using the Differential Evolution (DE) algorithm, which establishes precise CT image thresholds for segmentation. The efficacy of the proposed framework is substantiated through extensive experiments, which reveal its superior performance in segmenting liver CT images against complex backgrounds. The results affirm the framework's proficiency, particularly in medical imaging contexts with intricate backdrops, thereby underscoring its potential for enhanced diagnosis and therapeutic planning.

This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

This study presents a novel methodology for robust classification of image quality, a critical task in the domain of computer vision. The ability to accurately and promptly classify an image as being of inferior quality, due to factors such as lighting, focus, encoding, and compression, is crucial for a wide range of applications, including autonomous vehicles, web search technologies, smartphones, and digital cameras. Moreover, this capability holds significant potential for numerous industrial applications, particularly in the realm of quality assurance in manufacturing processes or outgoing inspections. In response to this requirement, a novel automated system is proposed herein, employing an optimization algorithm to categorize images into six distinct classes: motion blur, white noise, Gaussian blur, poor illumination, JPEG 2000, and high-quality reference images. The proposed framework is evaluated against existing methodologies using a selection of publicly available datasets. Both subjective and objective assessment results will be presented to demonstrate the efficacy of the proposed framework. This work underscores the potential of leveraging optimized deep learning techniques for robust and automatic image quality classification, thereby paving the way for improved quality assurance across diverse industries.

Introduction Saffron is one of the most coveted and one of the most tainted products in the global food market. A major challenge for the saffron industry is the difficulty to distinguish between adulterated and authentic dried saffron along the supply chain. Current approaches to analyzing the intrinsic chemical compounds (crocin, picrocrocin, and safranal) are complex, costly, and time-consuming. Computer vision improvements enabled by deep learning have emerged as a potential alternative that can serve as a practical tool to distinguish the pureness of saffron. Methods In this study, a deep learning approach for classifying the authenticity of saffron is proposed. The focus was on detecting major distinctions that help sort out fake samples from real ones using a manually collected dataset that contains an image of the two classes (saffron and non-saffron). A deep convolutional neural model MobileNetV2 and Adaptive Momentum Estimation (Adam) optimizer were trained for this purpose. Results The observed metrics of the deep learning model were: 99% accuracy, 99% recall, 97% precision, and 98% F-score, which demonstrated a very high efficiency. Discussion A discussion is provided regarding key factors identified for obtaining positive results. This novel approach is an efficient alternative to distinguish authentic from adulterated saffron products, which may be of benefit to the saffron industry from producers to consumers and could serve to develop models for other spices.

Early detection of brain tumors (BTs) can save valuable lives.BTs classification is usually accomplished by using magnetic resonance imaging (MRI), which is commonly carried out earlier than definitive talent surgery.Machine learning (ML) strategies can assist radiologists to diagnose tumors barring invasive measures.One of the challenges of traditional classifiers is that they rely on informative hand-crafted features, which can be a time-consuming process to extract.We proposed fully automatic framework for BTs classification with weighted contrast-enhanced MRI images.The proposed framework includes an enhancement preprocessing to improve input images quality and a classification phase for images classification into three classes of tumors (meningioma, glioma and pituitary tumor) and ordinary cases.The model was built used "Lightweight Convolutional Neural Network (LWCNN)" that allows to automatically extract features.We tested the LWCNN model in two experiments.In the first one, the model has been tested with original datasets.We tested our proposed framework on the same dataset after enhancing the features of MRI images in the second experiment.As per the experiment results, it has been observed that the proposed framework achieves the desired outcome which demonstrates the effectiveness of our proposed framework.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.

Most plant diseases have apparent signs, and today's recognized method is for an expert plant pathologist to identify the disease by looking at infected plant leaves using a microscope. The fact is that manually diagnosing diseases is time consuming and that the effectiveness of the diagnosis is related to the pathologist's talents, making this a great application area for computer-aided diagnostic systems. The proposed work describes an approach for detecting and classifying diseases in citrus plants using deep learning and image processing. The main cause of decreased productivity is considered to be plant diseases, which results in financial losses. Citrus is an important source of nutrients such as vitamin C all around the world. On the contrary, citrus diseases have a negative impact on the citrus fruit and quality. In the recent decade, computer vision and image processing techniques have become increasingly popular for the detection and classification of plant diseases. The suggested approach is evaluated on the citrus disease image gallery dataset and the combined dataset (citrus image datasets of infested scale and plant village). These datasets were used to identify and classify citrus diseases such as anthracnose, black spot, canker, scab, greening, and melanose. AlexNet and VGG19 are two kinds of convolutional neural networks that were used to build and test the proposed approach. The system's total performance reached 94% at its best. The proposed approach outperforms the existing methods.

Medical imaging is considered one of the most important areas within scientific imaging due to the rapid and ongoing development in computer-aided medical image visualisation, advances in analysis approaches, and computer-aided diagnosis. Here, the principles of quantum computation and information to develop the field of medical image processing will be reviewed. The advancement of quantum computation in image processing has proved its outstanding properties for processing and storage capacity compared to the classical methods. This review provides a comprehensive summary of the common advanced approaches, methodologies and advanced applications in medical images based on quantum computation.

Plant diseases are a major impendence to food security, and due to a lack of key infrastructure in many regions of the world, quick identification is still challenging. Harvest losses owing to illnesses are a severe problem for both large farming structures and rural communities, motivating our mission. Because of the large range of diseases, identifying and classifying diseases with human eyes is not only time-consuming and labor intensive, but also prone to being mistaken with a high error rate. Deep learning-enabled breakthroughs in computer vision have cleared the road for smartphone-assisted plant disease and diagnosis. The proposed work describes a deep learning approach for detection plant disease. Therefore, we proposed a deep learning model strategy for detecting plant disease and classification of plant leaf diseases. In our research, we focused on detecting plant diseases in five crops divided into 25 different types of classes (wheat, cotton, grape, corn, and cucumbers). In this task, we used a public image database of healthy and diseased plant leaves acquired under realistic conditions. For our work, a deep convolutional neural model AlexNet and Particle Swarm optimization was trained for this task we found that the metrics (accuracy, specificity, Sensitivity, precision, and F score) of the tested deep learning networks achieves an accuracy of 98.83%, specificity of 98.56%, Sensitivity of 98.78%, precision of 98.67%, and F-score of 98.47%, demonstrating the feasibility of this approach.

COVID-19 has been considered one of the recent epidemics that occurred at the last of 2019 and the beginning of 2020 that world widespread. This spread of COVID-19 requires a fast technique for diagnosis to make the appropriate decision for the treatment. X-ray images are one of the most classifiable images that are used widely in diagnosing patients' data depending on radiographs due to their structures and tissues that could be classified. Convolutional Neural Networks (CNN) is the most accurate classification technique used to diagnose COVID-19 because of the ability to use a different number of convolutional layers and its high classification accuracy. Classification using CNNs techniques requires a large number of images to learn and obtain satisfactory results. In this paper, we used SqueezNet with a modified output layer to classify X-ray images into three groups: COVID-19, normal, and pneumonia. In this study, we propose a deep learning method with enhance the features of X-ray images collected from Kaggle, Figshare to distinguish between COVID-19, Normal, and Pneumonia infection. In this regard, several techniques were used on the selected image samples which are Unsharp filter, Histogram equal, and Complement image to produce another view of the dataset. The Squeeze Net CNN model has been tested in two scenarios using the 13,437 X-ray images that include 4479 for each type (COVID-19, Normal and Pneumonia). In the first scenario, the model has been tested without any enhancement on the datasets. It achieved an accuracy of 91%. But, in the second scenario, the model was tested using the same previous images after being improved by several techniques and the performance was high at approximately 95%. The conclusion of this study is the used model gives higher accuracy results for enhanced images compared with the accuracy results for the original images. A comparison of the outcomes demonstrated the effectiveness of our DL method for classifying COVID-19 based on enhanced X-ray images.

The proposed work describes an approach for the segmentation of abnormal lung CT scans of COVID-19. Lung diseases are the leading killer in both men and women. The pulmonary experts normally make attempts, such as early detection of patients by tomography tests before lung specialists treat patients who are tortured by lung disease. Moreover, lung specialists do their best to detect the presence of lung conditions. X rays or CT scan checks are performed for tomography tests. The finest approach for medical diagnosis and a wide range of uses is computed tomography (CT). This kind of imaging offers elaborate cross-sectional pictures of skinny slices of the organic structure. However, the preprocessing and denoising methods of Lung CT scans may mask some important image features. To address this challenge, we propose a novel framework involving an optimization technique algorithm to solve a multilevel thresholding problem based on information theory to segment abnormal lung CT scans. The proposed framework will evaluate a sample of CT scan images taken from a well-known benchmark database. The evaluation results will assess subjectively and objectively to demonstrate the effectiveness of the proposed framework.

Edge detection is the diverse way used to detect boundaries in digital images. Many methods exist to achieve this purpose, yet not all of them can produce results with high detection ratios. Some may have high complexity, and others may require numerous inputs. Therefore, a new multi-phase algorithm that depends on information theory is introduced in this article to detect the edges of aerial images adequately in a fully automatic manner. The proposed algorithm operated by utilizing Shannon and Hill entropies with specific rules along with a non-complex edge detector to record the vital edge information. The proposed algorithm was examined with different aerial images, its performances appraised against six existing approaches, and the outcomes were assessed using three image evaluation methods. From the results, promising performances were recorded as the proposed algorithm performed the best in many aspects and provided satisfactory results. The results of the proposed algorithm had high edge detection ratios as it was able to capture most of the significant edges of the given images. Such findings make the proposed algorithm desirable to be used as a key image detection method with other image-related applications.