The demand for creating automatic text summarization methods has significantly emerged as a result of the web’s explosive growth in textual data and the challenge of finding re-quired information within this massive volume of data. Multi-document text summarizing (MDTS) is an effective method for creating summaries by grouping texts that are relevant to a similar subject. With the aid of optimization methods, this strategy can be optimized. The majority of optimization algorithms used in the scientific literature are single-objective ones, but more recently, multi-objective optimization (MOO) techniques have been created, and their findings have outperformed those of single-objective methods. Metaheuristics-based techniques are also increasingly being used effectively in the study of MOO. The MDTS issue is therefore solved by the Multi-Objective Ant Colony Optimization (MOACO) method. This multi-objective metaheuristic algorithm is based on the Pareto optimization. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics have been used to assess the outcomes of experiments using Document Understanding Conferences (DUC) datasets. Additionally, they have consistently outperformed other referenced summarizer systems.

There have been several initiatives taken to guarantee higher yields and higher-quality crops as the agriculture sector grows. The agriculture industry is severely impacted by plant and agricultural illnesses and deficits. Several techniques and technologies have been developed to aid in the diagnosis, management, and eventual eradication of plant diseases. The efficient and accurate identification of plant diseases could be aided by the development of a quick and accurate model. The use of deep convolutional neural networks for image categorization has greatly improved accuracy. In this paper, we present a framework for automating disease detection by the use of a tailored DL architecture. Both the Plant Village dataset and the real-time field dataset are utilized in the testing process. Our model’s results are compared to those of other spatial exploitation models. The results show that the proposed method is superior to the standard deep-learning classifier. This proves the network’s potential for usage in real-time applications by extracting high-level features that boost the efficiency and accuracy while reducing the risk introduced by a manual procedure. In order to enable a prompt reaction, and perhaps a targeted pesticide application, the suggested method has the ability to provide the early diagnoses of plant vital health.

Due to the lack of precise medical testing for autism, such as blood tests to detect the illness, diagnosing autism spectrum disorder (ASD) has proven to be challenging. The prevalence of restrictive and/or repetitive behaviors and difficulties and impairments in social communication are hallmarks of autism spectrum disorders. This behavioral condition has been identified. Doctors assess the child's developmental history and behavior to make a diagnosis. Research results. This research used a hybrid Multi Label-Graph Convolutional Network (ML-GCN) with label-attentive neighborhood convolution to categorize the autism spectrum disorder. It offers a clear and effective graph wrapper module in particular for collecting the local attribute data of a specific node to produce a logical representation of node functioning. Additionally, the homeopathic theory recommends developing a taxonomy for attention-related terms. Furthermore, developed an adaptive graph technique that allows the model to learn the kernel for each layer dynamically and uniquely, allowing the model to acquire more valuable and efficient features. On three frequently used reference datasets, including customized and non-specialized networks, comprehensive tests were conducted to validate the neural network-based approach to multi-label classification.

The scope to express opinion by the end-user or customer about a product, service, administrative decisions, or events to the world is phenomenal, which is due to the internet access feasibility and the ease of using social web. The opportunity to identify the divergence of the opinion about the corresponding entity (product, service, decision, or event) enables to conclude the opportunity to stabilize, revise, improve, or withdrawal of that entity in regard to amaze the target audience. However, the quantity of the opinions about an entity elevates the difficulty of process to identify the divergence of the opinion using human resource. Hence, the computer-aided methods are the crux to find the divergence of the opinions. Nevertheless, the projection of opinion is not mandate to be in similar passion. Hence, the divergence in nature of expressing the opinion is substantial due to the ambiguity in natural languages and the dimensions of opinion expression feasibilities. In regard to this argument, the design of computer-aided methods to identify the opinion scope is intended in recent past contributions of the research contributions related to natural language processing, which are still inadequate. In order to this argument, the contribution of this manuscript ‘Method of Optimizing the Dimensional Features (MODF)in Sentiment Analysis’, this is a machine learning technique that enables to deal the multiple dimensions of the expressions of the opinions to train the target classifier. The experimental study portraying the significance and scalability of the proposal scaled against the other contemporary models.