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Against the backdrop of increasing research on the optimization and application of intelligent technology, this study reviews the relevant literature from the perspectives of machine intelligence and intelligent systems, aiming to offer new research ideas for achieving sustainable agricultural development. By analyzing 1,611 Chinese and foreign literatures from 2000 to the present, we have found that research in the field of machine intelligence and intelligent systems has experienced a three-stage development pattern of fluctuating upward, explosive growth, and slowing down through adjustment, reaching a peak in 2022. China, India, and the United States are the main countries conducting research in this field. Although cooperation among countries is frequent, collaboration between authors is less common, and academic connections are relatively weak. Currently, the field of intelligent systems is moving toward diversified development. Researchers can further enhance studies in the interdisciplinary areas of intelligent technology, laying the foundation for the application of intelligent systems and machine intelligence in sustainable agriculture.

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image’s semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment.

In general biologists are not accustomed to formulating biological problems in the precise mathematical terms that are required to solve the problems analytically or numerically. Although many computational tools for systems biology have been developed recently, our observations indicate that many of these tools are powerful only in the hands of those who know a lot about how to use them. For most biologists, the tools have a protracted learning curve and unfriendly user interface that often diminish their likelihood of being used.

Our long-term goal is to build a knowledge system that allows biologists to synthesize complex biological systems via natural language interactions, and the system is able to generate the corresponding mathematical descriptions so that the often cumbersome communication process between biologists and mathematicians/engineers in formulating complex biological problems in mathematic terms can be performed more easily.

To focus, the first goal in this research is to build a knowledge system prototype that focuses on transport related biological problems that occur from the cellular to tissue level. We address specifically two inter-related problems: (1) Provision of an intelligent system that is capable of automatically synthesizing smaller components into more complex systems; Provision of a user-friendly and natural language interface.

In order to improve the efficiency of the e-commerce logistics system, this paper analyzes the application of dynamic bandwidth resource allocation algorithm in intelligent logistics tracking scenarios and distribution scenarios, and achieves the purpose of optimizing the allocation of bandwidth resources by changing the sampling rate of the control signal. Moreover, this paper designs and implements an e-commerce logistics information system based on the Internet of Things, and starts with each functional module to introduce in detail the various functions realized by the system in this paper. This paper changes the traditional logistics operation mode, through the realization of various functional modules, users can grasp personal historical orders and view the list of historical orders. Finally, this paper analyzes the performance of this system through experimental research. The results of the research show that the e-commerce logistics system based on the Internet of Things proposed in this paper is effective.

In this paper the voltage/current characteristics and the effect of NO₂ gas on the electrical conductivity of a PbPc gas sensor array is studied. The gas sensor is manufactured using vacuum deposition of gold electrodes on sapphire substrate with the lead-phathalocyanine vacuum sublimed on the top of the gold electrodes. In a comparison between two versions of the PbPc gas sensor array, it was found that they differ in gap sizes between the deposited gold electrodes. The sensors are tested at different temperatures to account for conductivity changes as the molecular adsorption/desorption rate is affected by heat. The obtained results are found to be encouraging as the sensors showed stability and sensitivity towards low concentrations of applied NO₂ gas.

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy (FE-SEM) images. The processing scheme adopted in the proposed system focused on two steps. The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator. A problem arises from the question of how to extract features which characterize cervical precancerous cells. For the first step, a preprocessing technique called intensity transformation and morphological operation (ITMO) algorithm used to enhance the quality of images was proposed. The algorithm consisted of contrast stretching and morphological opening operations. The second step was to characterize the cervical cells to three classes, namely normal, low grade intra-epithelial squamous lesion (LSIL), and high grade intra-epithelial squamous lesion (HSIL). To differentiate between normal and precancerous cells of the cervical cell FE-SEM images, human papillomavirus (HPV) contained in the surface of cells were used as indicators. In this paper, we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture. Gray level co-occurrences matrix (GLCM) technique was used to extract the texture features. To confirm the system’s performance, the system was tested using 150 cervical cell FE-SEM images. The results showed that the accuracy, sensitivity and specificity of the proposed system are 95.7%, 95.7% and 95.8%, respectively.

Designing intelligent diagnosis of prostate diseases in intelligent medical assistance systems has gradually become a research hotspot. However, rectal ultrasound (TRUS) as the main diagnostic tool for prostate diseases remains a challenging issue. (1) Due to limited prostate TRUS imaging data, it is difficult to train a robust deep learning model. (2) In terms of visual features, ultrasound images of prostate cancer are similar to TRUS images of other tissues and organs, so it is difficult for a single neural network model to accurately learn the feature representation of the disease. To address the above problems, we first establish a high-quality dataset for prostate TRUS imaging, and then design multi teacher knowledge distillation to achieve accurate disease recognition. The experimental results show that, compared with knowledge distillation without a teacher model and a single teacher model, knowledge distillation using multiple teacher models can significantly improve the accuracy of prostate TRUS image cancer prediction. As the number of teacher models increases, the accuracy rate is further improved, which verifies the effectiveness of this method in intelligent systems.

Close range photogrammetry is an image-based method of measurement that can be used to create a three-dimensional model of objects. This method is very popular due to its high speed, low cost, and non-invasiveness as a measurement tool in medicine. The main weakness of these systems is the lack of topology between points, which is especially important in the medical field. The neural gas network can learn the topology of the points associated with the 3D model of objects. Considering the capabilities mentioned regarding the photogrammetry and the neural gas network, a combination of these can be used as a diagnostic tool in such a way that in addition to considering the points of the model, neighboring points are also considered in the diagnostic process. Accordingly, in this study, we want to design a system by combining these two tools that can recognize diseases by their apparent symptoms. Moreover, the use of the neural gas network and the possibility of local and general examination of the organs increase the accuracy of diagnosis. Diagnosis of foot disease has been used as a case study in this system. The results showed that the neural gas network has a high degree of flexibility for modeling the human body compared to previous methods, and provides a better approximation. Also, the accuracy of reconstruction of the 3D model of the object is effective in the process of diagnosis and influences the level of intelligence of the system as well. Finally, in the system implemented, the results showed that the disease was correctly diagnosed in all 5 feet of the patient. Also, in 4 cases, the location of the disease was correctly detected and in one case the location of the disease was misdiagnosed.

The multi-joint shipborne crane system worked in the sea is researched in this paper. It is affected by many kinds of disturbance. The basic fuzzy control's adaptable capacity to the variation of environment conditions and system's parameter with time is not very good for this system. In order to improve the controlled system's robustness and its response characteristic, the parameters of the fuzzy membership function be optimized automatically according to the genetic algorithm (GA) encoded by matrix individual was designed. The designs of controller and optimization algorithm are introduced. The meaning of the optimized parameters is clear. The simulation of the shipborne crane system was completed. The control features of this system are largely improved comparing to the basic fuzzy control.

If information technology can be utilized for campus safety, it would be helpful for school staff to monitor all situations in schools. This study is based on campus safety management and is aimed at establishing an intelligent mobile information system in colleges; this will facilitate the installation of video recorders in the rush areas (at the entrance and exit of the campus) and inconspicuous places on campuses. This study adopts the Windows Media Player along the RTP/RTSP protocol in order to embed the mobile information system into the users' machines (personal digital assistants or smart phones). In this study, we randomly select 40 school staff and 100 students to test the intelligent system. Further, the results were compared with those from a conventional school safety system. Forty-two percent of users were satisfied with the conventional safety system and 96% of the users were satisfied with the intelligent mobile system when using personal digital assistants. The software integrity satisfaction was 99.99%; usability satisfaction, 96%; correctness, 95%; and reliability, 95%.

In order to improve the efficiency of product design and reuse in heterogeneous system of knowledge sharing, this paper introduced the concept of ontology into product variant design, and grinding machine design was as an example. A lot of experience and accumulated knowledge in product design was shared and reused. It is precisely to formulate ontology knowledge such as variant design features and parameter, and applied the software protégé4.3 to construct ontology model, as well as runed resoning on model data information. It developed a set of complete product intelligent system of variant design, which can effectively solve the problem of the repeated design and greatly shorten product development cycle.