Introduction: The ethical dimensions of agriculture need to be explored, distinguishing between ethics in agriculture and ethics of agriculture. Ethics in agriculture is traditionally based on abstract moral theory, formulated through logical reasoning, but often found inadequate to address the complex ethical challenges within agriculture due to its abstract nature and lack of practical visibility. Conversely, ethics of agriculture is approached within a context-specific framework derived from the research topic with the aim of providing more tailored solutions. Materials and Methods: A comprehensive review of the relevant literature is conducted, using a descriptive-analytical approach to provide a conceptual framework for understanding agricultural ethics. Conclusion: The article highlights the critical need for a comprehensive ethical framework to address the multifaceted ethical challenges in the agricultural sector. The importance of analyzing and resolving ethical dilemmas at all stages of agriculture is underscored in order to align practices with human ideals and to ensure ethical decision-making by stakeholders involved in agriculture.

Automatic topic Detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic Detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-Detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic Detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Background & Aims: One of the symptoms of lung cancer, which is one of the deadliest cancers, is the lung nodules. It is very difficult to detect these tiny nodules on CT scans of the lungs with the naked eye. Therefore, intelligent systems or computer-aided Detection (CAD) systems can assist a radiologist in detecting, locating, and evaluating the quality of lung nodules. The most important challenge of existing intelligent systems is the balanced improvement of accuracy, sensitivity, specificity, and reduction of false positive rate (FPr), and also the complexity of these systems has reduced the efficiency and speed of execution. Therefore, the purpose of this study was to provide an agile framework and optimize the challenge. Materials & Methods: One of the new subfields of artificial intelligence is the deep learning and orientation of CNN networks, which has been widely used in the analysis of medical images in recent years. In this research, an innovative network based on CNN networks of LeNet type is proposed to extract image features as well as image classification. The used dataset is a subset of 7072 image pieces derived from the LIDC-IDRI standard dataset. The size of nodules of these images, which are used to train and validate the network, are 1 to 4 mm. Results: The training and validation processes of this network were performed with a computer device (configurations 2. 4GHz Core i5 processor, 8GB of memory, and Intel Graphics 520) in five hours and eleven minutes and the accuracy, sensitivity, and specificity are 91. 1%, 85. 3% and 92. 8%, respectively. Conclusion: Based on the standard basis of the proposed model and also the use of valid database images to measure the network and compare with previous works, the results establish a good balance between evaluation criteria, and with faster implementation gain the necessary capability for real time applications.

Using sensors and actuators as engine control mechanisms brings technical complexity to rule-based approach to diagnosis as it is difficult to establish a complete association between sensor data and the symptoms. Diagnostic evaluation of critical components in vehicle engines has only gained little attention, whereas the interdependent nature of sensors and propeller requires continuous monitoring for stability and temperature control. In this study, the Bayesian probability approach was used to provide intelligence logic with mathematical formulation for detecting overheating in vehicle engines; by providing the architectural design of the proposed system.The proposed framework was implemented using Microsoft Visual Basic.NET with integrated ActiveX Data Object (VB ADO) to experiment with the model for performance evaluation. Its usability testing and computational pattern were carried out with comparative analysis. Therefore, this study recommended that the problem domain for automobile diagnosis should be explicit about inculcating all engine-related problems other than overheating.

Network communication shows a variety of issues with the fast expansion of computer devices, ranging from network administration to traffic engineering. A well-known method for improving these connections is Software-Defined Networking (SDN). The SDN is a networking architecture that separates the control plane from the data plane to ease network administration. The main advantage of the SDN is the central controller. However, it has security flaws like unreachability in Distributed Denial-of-Service attacks (DDoS). Hence, defending SDN against DDoS attacks is critical. We proposed a framework for detecting DDoS attacks and a fault-tolerant method to replace faulty leader controller in distributed multi-controller SDN. We used multi-controllers architecture and leader election algorithm to present a fault-tolerant framework to select a new leader controller, in the case of a leader controller failure. In addition, an early DDoS attack Detection algorithm using the entropy of destination IP addresses and the packet window initiation rate is presented. To evaluate our proposed method in various configurations, we simulated exhaustive experiments in Mininet and Floodlight. The results show that our approach outperforms similar algorithms in various network configurations and multi-victim attacks.

The advent of cloud computing has made it simpler for users to gain access to data regardless of their physical location. It works for as long as they have access to the internet through an approach where the users pay based on how they use these resources in a model referred to as “pay-as-per-usage”. Despite all these advantages, cloud computing has its shortcomings. The biggest concern today is the security risks associated with the cloud. One of the biggest problems that might arise with cloud services availability is Distributed Denial of Service attacks (DDoS). DDoS attacks work by multiple machines attacking the user by sending packets with large data overhead. Therefore, the network is overwhelmed with unwanted traffic. This paper proposes an intrusion Detection framework using Ensemble feature selection with RNN (ERNN) to tackle the problem at hand. It combines an Ensemble of multiple Machine Learning (ML) algorithms with a Recurrent Neural Network (RNN).  The framework aims to address the issue by selecting the most relevant features using the ensemble of six ML algorithms. These selected features are then used to classify the network traffic as either normal or attack, employing RNN. The effectiveness of the proposed model is evaluated using the CICDDoS2019 dataset, which contains new types of attacks. To assess the performance of the model, metrics like precision, accuracy, F-1 score, and recall are taken into consideration.