In this chapter, the Internet of Things (IoT) system is required for automating irrigation systems and monitoring real-time data from sensors. IoT systems may easily and affordably integrate the long-range wide-area network (LoRaWAN). Four irrigation strategies, including ET (ETc), MP60 (watermark 200SS-5 soil matric potential sensors, (-70 kPa), MP50 (at -50 kPa)), and GesCoN (a decision support system), were developed and put to the test. According to the findings, treatment MP70 had a marketable yield that was greater by 16 percent and 24 percent than that of ET and MP50. Due to improper installation and positioning of the soil moisture sensors, MP40 received relatively little water during irrigation. The GesCoN and ET results were not significantly different from the MP70 results. It has been demonstrated that using sensors and precision irrigation can help farmers conserve water when growing crops. The LoRaWAN-based IoT system nevertheless performed admirably in terms of power usage, connectivity, sensor reading, and valve management.

Detecting sarcasm in text can be challenging, but it's an opportunity to exercise the ability to consider various perspectives and aspects. Considering multiple viewpoints while analysing sentences can lead to comprehensive results and constructive discussions. Sentiment analysis algorithms are constantly improving and becoming more accurate in identifying sarcasm, even in implicit expressions where the sarcasm is not explicitly stated. It's exciting to have the opportunity to detect sarcasm conveyed through subtle cues, as it adds another layer of complexity to the task. The field of sarcasm detection has made great strides with the use of supervised classification models that can accurately distinguish between sarcastic and non-sarcastic sentences through labelled data. This approach shows promise for identifying explicitly incongruous statements, and there is an increasing need for techniques that can successfully detect sarcasm in sentences with implicit sentiment incongruity. This study employs a deep learning model to tackle the challenge of distinguishing implicit sarcasm. The model being used is a recurrent neural network (RNN), which has the ability to retain numerical representations of previously processed information. The proposed model is designed to effectively identify sarcasm by capturing context-based incongruity within the presented text, which is a crucial factor. The proposed model, Proposed LSTM (P-LSTM), effectively captures the dependencies among words in both suffixes and prefixes by scanning the given sentences bi-directionally. The classification is organised into two levels: emotional and semantic. The suggested approach offers a way to accurately evaluate and categories tweets based on their sarcastic state. Excitingly, the proposed method is being evaluated through experiments on two automatically annotated datasets and two manually annotated datasets to determine its effectiveness. The suggested model is compared to numerous state-of-the-art methods, highlighting its efficacy. The findings demonstrate that the suggested model is highly effective for sarcasm detection compared to other methods currently in use. The suggested model has great potential in recognising implicit sarcasm by utilising deep learning and context-based incongruity analysis, which could lead to significant improvements over existing methods.

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.