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Quality assessment of fruits plays a key part in the global economy’s agricultural sector. In recent years, it has been shown that fruits are affected by different diseases, which can lead to widespread economic failure in the agricultural industry. Traditional manual visual grading of fruits could be more accurate, making it difficult for agribusinesses to assess quality efficiently. Automatic grading of fruits using computer vision has become a prominent area of study for many researchers. In this study, a deep learning-based model called FruitVision is proposed for the automatic grading of various fruits. The results showed that FruitVision performed all the existing models and obtained an accuracy of 99.42, 99.50, 99.24, 99.12, 99.38, 99.38, 99.17, 98.86, and 97.96% for the apple, banana, guava, lime, orange, pomegranate, Ajwa date, Mabroom date, and mango, respectively, using 5-fold cross-validation. This is a remarkable achievement in the field of AI-based fruit grading systems.

This research article outlines a study that examines the creation of a comprehensive knowledge graph specifically designed for the domain of urban agriculture. The research centers on the acquisition, synthesis, and arrangement of pertinent information from various origins in order to establish a specialized knowledge graph tailored for urban agricultural systems. The graph depicts the interrelationships and attributes of various entities, including urban farms, crops, farming methods, environmental factors, and economic elements. Moreover, this study investigates the efficacy of different graph embedding methodologies in the domain of urban agriculture. The aforementioned techniques are utilized in the context of the urban agriculture knowledge graph in order to extract significant representations of entities and their relationships. The primary objective of the experimental study is to investigate and reveal semantic relationships, patterns, and predictions that have the potential to improve decision-making processes and optimize practices in the field of urban agriculture. The results of this study make a significant contribution to the existing body of knowledge in the area of urban agriculture. Additionally, they offer valuable insights into the potential uses of graph embedding techniques within this field.

In 2019, lung disease severely affected human health and was later renamed coronavirus disease 2019 (COVID-2019). Since then, several research methods have been proposed, such as reverse transcription polymerase chain reaction (RT-PCR), and disease identification through chest X-rays and computed tomography (CT) scans, to help the healthcare sector. RT-PCR was time-consuming when more patients were present, and a CT scan was costly. Several deep-learning (DL) methods were used to identify diseases using computer-aided tools. Among those convolutional neural networks (CNNs), the state of the art was adopted in the machinery to predict cancer. However, there is a lack of explainability (XAI) in how CNN predicts the disease. In this article, we construct XAI ensembles with Local Interpretation Model Agnostic Explanation(LIME), Grad CAM, and a Saliency map. It provides a visual explanation for a DL prognostic model that predicts COVID-19 respiratory infection in patients. Our quantitative experimental results have shown that ensemble XAI with an accuracy of 98.85%, although individual LIME has scored an accuracy of 99.62% on test data, is more reliable since it is the combination of models.

In recent years, there has been a noticeable rise in the mortality rate, and heart disease is a significant contributor to this trend. According to the California Association for Diabetes Investigation, by 2015, cardiovascular disease would be the primary cause of death in India, where 62 billion people live. Deficiencies in the heart’s ability to pump blood to and from the rest of the body are the leading cause of cardiovascular disease. The healthcare industry is a prime example of a sector poised to greatly benefit from the availability of massive amounts of data and analytical insights. Increasingly, it will be important to extract medical data to predict and treat the high fatality rate caused by heart attacks. Every day, humanity generates terabytes worth of data. Medical errors with dire effects can be avoided only with high-quality services. Hospitals can reduce the price of expensive clinical testing by using decision support systems. Hospitals in the modern-day use hospital information systems to keep track of patient records. The health care sector generates vast amounts of data, but little of it is really put to good use. It will be important to adopt a new strategy to reduce costs and make accurate predictions about heart disease. To determine which machine learning and deep learning approaches are most useful and accurate for predicting and classifying cardiac illnesses, this article reviews the existing literature on the topic and subsequently tries to detect the most probable factors leading to heart disease. This study introduces and models an artificial neural network methodology for identifying potential cardiovascular disease risk factors. In this study, we examine and present the various full and partial correlations among risk attributes. In addition, a number of risk variables are analysed to generate a predicted list of risk features most likely to result in cardiovascular disease.

The technology-driven modern age emphasizes the security and privacy of communication. Through this paper, we delve deep into the need for user-centric security within cloud-based environments. The need for enhancement in encryption arises due to the increasing cases of data breaches and insider threats in cloud-based environments recently. The focus is laid upon the use of RSA encryption, end-to-end encryption, and anonymous messaging to address security-based concerns. The primary focus of this research is to develop a comprehensive security system to ensure the confidentiality and authenticity of text-based messages shared in the cloud. The proposed improved RSA algorithm, as suggested, incorporates three prime numbers in the key generation process. To address repetitive ciphertext, the proposed algorithm involves adding the index of each character in the plaintext string to the character's integer value before encryption. Conversely, during decryption, the same index is subtracted. This proposed algorithm has been utilized in a practical scenario, specifically in the implementation of a chat application. This paper presents a proof-of-concept for the proposed enhanced version of the RSA algorithm, accompanied by a thorough comparison and analysis of computational times across various bit lengths. Increase in data security at a cost of minor increase in computation time was observed through this research. [ABSTRACT FROM AUTHOR]

Advanced Driver Assistant System(ADAS) is a critical component of self-driving cars, and lane detection is one of its applications. Convolution Neural Network (CNN) has exhibited its potential to devise state-of-the-art solutions for various computer vision problems and lane detection. However, different scales of lane lines, partial visibility of lane, and the varying resolution of hierarchical features challenge the CNN-based detection methods to localize the lane accurately. In this work, we propose a deep supervision-based model to detect the partially visible lane lines. Hierarchical (Deep) supervision helps the proposed model to trace and classify the lane features at different scales. The simulation of the proposed model gives robust results with partial visibility of lane paint compared to an existing and proven model. The proposed model's accuracy is as high as 92% for partially visible lane and 99% for full visible lane, which stands to the right in its peer work comparison. [ABSTRACT FROM AUTHOR]

Recently, an IoT device has taken over as the primary platform for botnet operations. Further research is required to build the proper detection techniques based on the new aspects of botnet assaults since they are not entirely safe. This study aims to develop a parametric analysis of the suggested MAOA hybrid optimization model for intelligent botnet attack detection. The model consists of the extraction of particular features, Improved Information Gain based feature selection, and a hybrid classification-based attack detection model "(Bidirectional Gated Recurrent Unit (BI-GRU)) and Recurrent Neural Network (RNN)," where the training weights of BI-GRU are tuned optimally by MAOA algorithm. Finally, parametric and non-parametric analysis is done to evaluate the performance of the proposed work. [ABSTRACT FROM AUTHOR]