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Tomato plants are vulnerable to a broad number of diseases, each of which has the potential to cause significant damage. Diseases that affect crops substantially negatively impact the quantity and quality of agricultural products. Regarding quality crop maintenance, the importance of a timely and accurate diagnosis cannot be overstated. Deep learning (DL) strategies are now a critical research field for crop disease diagnoses. One independent system that can diagnose plant illnesses based on their outward manifestations is an example of an intelligent agriculture solution that could address these problems. This work proposes a robust hybrid convolutional neural network (CNN) diagnostic tool for various disorders that may affect tomato leaf tissue. A CNN and an inception module are the two components that make up this hybrid technique. The dataset employed for this study consists of nine distinct categories of tomato diseases and one healthy category sourced from PlantVillage. The findings are promising on the test set, with 99.17% accuracy, 99.23% recall, 99.13% precision, 99.56% AUC, and 99.17% F1-score, respectively. The proposed methodology offers a solution that boasts high performance for the diagnostics of tomato crops in the actual agricultural setting.

In own global economy, the agricultural sector plays a pivotal role in every aspect of our modern life. One of the most important issues that matters in agriculture, that leads to huge economic losses, are the crop diseases. The reliable and accurate diagnosis of plant diseases, even today, remains one of the most difficult tasks. An efficient, accurate and rapid diagnosis of plant disease is active area of research. One of the solutions that has been proposed is Deep Learning (DL). DL is a vital approach in many fields, including agriculture, as it has the potential to reach a high level of accuracy and efficiency. Various authors have investigated DL techniques for agriculture, but most of them examine a very limited dataset or few models and optimizers. In constrast with existing publications, we have performed the most thoroughly examination of all the state of the art DL models resulting in discovering the best models and parameters for utilizing DL in modern agricalture. The experimental results have shown that the DenseNet201 model in combination with the Adam optimization algorithm achieves the highest testing accuracy score of 99.87% surpassing all other DL architectures.

In sensitive communications, the cryptographic hash function plays a crucial role, including in the military, healthcare, and banking, ensuring secure transmission by verifying data integrity and carrying out other vital tasks. Compared to other cryptographic hash algorithms, such as SHA-1 and SHA-2, the Keccak hash function (SHA-3) boasts superior hardware performance and is more resilient to modern cryptanalysis techniques. Nonetheless, hardware performance enhancements, such as boosting speed or reducing area usage, are constantly required. This research focuses on increasing the Keccak hash algorithm’s throughput rate by introducing a novel architecture that reduces the total number of clock cycles required to obtain the result of a hash function. Additionally, the new simplified structure of the round constant (RC) generator design assures a reasonably low area and achieves the highest throughput and efficiency. Thus, when implemented, it achieved the highest throughput of 19.515 Gbps, 24.428 Gbps, 33.393 Gbps, and 36.358 Gbps on FPGA devices with the Virtex-5, Artix-7, Virtex-6, and Virtex-7, respectively. Finally, our approach is compared to recently published designs.

At the end of 2019, a severe public health threat named coronavirus disease (COVID-19) spread rapidly worldwide. After two years, this coronavirus still spreads at a fast rate. Due to its rapid spread, the immediate and rapid diagnosis of COVID-19 is of utmost importance. In the global fight against this virus, chest X-rays are essential in evaluating infected patients. Thus, various technologies that enable rapid detection of COVID-19 can offer high detection accuracy to health professionals to make the right decisions. The latest emerging deep-learning (DL) technology enhances the power of medical imaging tools by providing high-performance classifiers in X-ray detection, and thus various researchers are trying to use it with limited success. Here, we propose a robust, lightweight network where excellent classification results can diagnose COVID-19 by evaluating chest X-rays. The experimental results showed that the modified architecture of the model we propose achieved very high classification performance in terms of accuracy, precision, recall, and f1-score for four classes (COVID-19, normal, viral pneumonia and lung opacity) of 21.165 chest X-ray images, and at the same time meeting real-time constraints, in a low-power embedded system. Finally, our work is the first to propose such an optimized model for a low-power embedded system with increased detection accuracy.

Hash functions are an essential mechanism in today’s world of information security. It is common practice to utilize them for storing and verifying passwords, developing pseudo-random sequences, and deriving keys for various applications, including military, online commerce, banking, healthcare management, and the Internet of Things (IoT). Among the cryptographic hash algorithms, the Keccak hash function (also known as SHA-3) stands out for its excellent hardware performance and resistance to current cryptanalysis approaches compared to algorithms such as SHA-1 and SHA-2. However, there is always a need for hardware enhancements to increase the throughput rate and decrease area consumption. This study specifically focuses on enhancing the throughput rate of the Keccak hash algorithm by presenting a novel architecture that supplies efficient outcomes. This novel architecture achieved impressive throughput rates on Field-Programmable Gate Array (FPGA) devices with the Virtex-5, Virtex-6, and Virtex-7 models. The highest throughput rates obtained were 26.151 Gbps, 33.084 Gbps, and 38.043 Gbps, respectively. Additionally, the research paper includes a comparative analysis of the proposed approach with recently published methods and shows a throughput rate above 11.37% Gbps in Virtex-5, 10.49% Gbps in Virtex-6 and 11.47% Gbps in Virtex-7. This comparison allows for a comprehensive evaluation of the novel architecture’s performance and effectiveness in relation to existing methodologies.

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector.

The prompt and accurate identification of the causes of pneumonia is necessary to implement rapid treatment and preventative approaches, reduce the burden of infections, and develop more successful intervention strategies. There has been an increase in the number of new pneumonia cases and diseases known as acute respiratory distress syndrome (ARDS) as a direct consequence of the spread of COVID-19. Chest radiography has evolved to the point that it is now an indispensable diagnostic tool for COVID-19 infection pneumonia in hospitals. To fully exploit the technique, it is crucial to design a computer-aided diagnostic (CAD) system to assist doctors and other medical professionals in establishing an accurate and rapid diagnosis of pneumonia. This article presents a robust hybrid deep convolutional neural network (DCNN) for rapidly identifying three categories (normal, COVID-19 and pneumonia (viral or bacterial)) using X-ray image data sourced from the COVID-QU-Ex dataset. The proposed approach on the test set achieved a rate of 99.25% accuracy, 99.10% Kappa-score, 99.43% AUC, 99.24% F1-score, 99.25% recall, and 99.23% precision, respectively. The outcomes of the experiments demonstrate that the presented hybrid DCNN mechanism for identifying three categories utilising X-ray images is robust and effective.

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases.

Presently, cryptographic hash functions play a critical role in many applications, such as digital signature systems, security communications, protocols, and network security infrastructures. The new standard cryptographic hash function is Secure Hash Algorithm 3 (SHA-3), which is not vulnerable to attacks. The Keccak algorithm is the winner of the NIST competition for the adoption of the new standard SHA-3 hash algorithm. In this work, we present hardware throughput optimization techniques for the SHA-3 algorithm using the Very High Speed Integrated Circuit Hardware Description Language (VHDL) programming language for all output lengths in the Keccak hash function (224, 256, 384 and 512). Our experiments were performed with the Nios II processor on the FPGA Arria 10 GX (10AX115N2P45E1SG). We applied two architectures, one without custom instruction and one with floating point hardware 2. Finally, we compare the results with other existing similar designs and found that the proposed design with floating point 2 optimizes throughput (Gbps) compared to existing FPGA implementations.

The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) for rapid and accurate identification of lung diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing the five-fold cross-validation method to ensure the robustness of our results, our CNN model, optimized for heterogeneous embedded devices, demonstrated superior diagnostic performance. It achieved a 98.56% accuracy, outperforming established networks like ResNet50, NASNetMobile, Xception, MobileNetV2, DenseNet121, and ViT-B/16 across precision, recall, F1-score, and AUC metrics. Notably, our model requires significantly less computational power and only 55 minutes of average training time per fold, making it highly suitable for resource-constrained environments. This study contributes to developing efficient, lightweight networks in medical image analysis, underscoring their potential to enhance point-of-care diagnostic processes.