IJLAI Transactions on Science and Engineering
IJLAI Transactions on Science and Engineering aims to become an international forum for the research and development community to publish articles of highest quality in all disciplines of science and engineering. Papers relating directly or indirectly to all aspect of these fields are also welcome to submit. IJLAI Transactions on Science and Engineering is intended to provide a forum for the expression of new ideas, as well as a place for exposition of these areas of knowledge that can further understanding of science and engineering issues and concerns. IJLAI Transactions on Science and Engineering at MIRC-PK is interested in publishing articles derived from any Science and Engineering field-based exposures along with conceptually robust theory rooted in the multidisciplinary.
Muslim Intellectuals Research Center, Multan-Pakistan (MIRC-PK)
Intelligent Rail Transit Research: The Application of Dynamic Distance Measurement in Gauge Detection System
Manual operation of gauge calibrator is tedious and time-consuming. The automatic transformation of calibrator is realized through the integration of motion control and machine vision technology. Two closed-loop positioning subsystems are composed of digital caliper length standard, chain drive mechanism and stepping motor to realize automatic motion control of gauge and ultra-high parameter measurement points. According to the luminance, geometric shape structure and fitting feature information, all kinds of indicator features and their relationships are identified in the display dial image to realize machine vision reading. The noise is removed by image difference method, the image is binarized by adaptive iterative threshold method, and the cross section contour of the track is obtained by expansion and thinning algorithm. The experimental results show that this method can effectively realize the high-precision dynamic measurement of gauge parameters.
Copyright (c) 2025 IJLAI Transactions on Science and Engineering
Large Language Model Fine-tuning Method Based on Adaptive Quantization
In recent years, large language models (LLMs) have excelled in comprehensive AI tasks such as language text generation, mathematics, abstraction, and code, and people have seen the embryonic form of general artificial intelligence. However, the fine tuning of the model also needs to consume a lot of computer memory, and the computing resources are extremely high, which is difficult to meet the general consumer grade graphics card. Therefore, an adaptive quantized low-rank (ADAQ-LoRA) fine-tuning algorithm is proposed to solve the problem of video memory consumption during fine-tuning of large language models. The solution is to use both quantification and pruning methods to dramatically reduce video memory usage without losing accuracy. ADAQ-LoRA is applied to ChatGLM2-6B model and its effectiveness is verified in different fine-tuning datasets and downstream scenarios. Compared with the existing large language model fine-tuning methods, ADAQ-LoRA shows better performance and lower memory usage.
Copyright (c) 2024 IJLAI Transactions on Science and Engineering
Sensor Principle and Application Course Ideological and Political Teaching Reform
"Sensor Principle and Application" is a compulsory professional course for Internet of Things engineering, electronic information engineering, intelligent perception and other majors. In view of the pain points existing in the teaching of this course, a series of innovative measures are put forward: integrating and updating the teaching content, adopting mixed teaching mode and innovative experimental methods, digging deeply into the curriculum ideology and politics, and building a multiple assessment and evaluation system, so as to comprehensively improve the learning effect of students and enhance their practical innovation ability and comprehensive quality. In order to promote the ideological and political teaching reform of the sensor Principle and application course, the research group combined the training requirements of engineering certification for students and the characteristics of application-oriented undergraduate students, with education moral education as the fundamental goal and task, to cultivate students' sense of innovation and feelings of home and country as the starting point, clear teaching objectives, use a variety of teaching methods, fully tap the integration point of moral education in the course, and optimize the assessment method. The ideological and political construction of professional courses will stimulate young students' confidence in the system, theory, road and culture to a greater extent, and train students to become application-oriented technical talents who love the Party, love the country and dare to innovate.
Analysis of problems and policies of management reform in higher education
Under the background of globalization and informatization, China's higher education management reform faces many challenges, including cumbersome administrative management, lack of transparency, and unequal distribution of resources. This paper aims to analyze these problems and put forward effective countermeasures. First of all, it is proposed to simplify the management process and enhance the transparency to improve the efficiency of decision-making and the fairness of resource allocation. Secondly, it emphasizes strengthening cooperation among universities, breaking barriers, promoting resource sharing and improving education quality. In addition, it is recommended to establish a comprehensive educational evaluation and supervision mechanism to ensure that colleges and universities follow rules and regulations and improve the credibility of education. This paper also discusses the key areas such as the reform of "management service", the integration of science and education, and the reform of personnel system, and puts forward specific reform measures in order to promote the modernization of higher education management system and enhance the international competitiveness of China's higher education.
Research on Challenges and Strategies of Career Development of Young Teachers in Application-oriented Colleges and Universities in the New Era
This paper aims to explore the challenges and strategies for the career development of young teachers in application-oriented colleges and universities in the new era. With the deepening of education reform, young teachers, as the main force of teachers in application-oriented colleges and universities, are increasingly concerned about their career development. The research points out that young teachers face many challenges in the process of career development, including the improvement of teaching skills, the stimulation of teaching motivation, and the pressure of survival and development. These challenges not only affect the teacher's personal career satisfaction and teaching effect, but also have a profound impact on the educational quality and personnel training of colleges and universities.
Revolutionizing Music Education: Embracing Innovation and Inclusivity
In this article, we explore the challenges facing music education in the 21st century and the need for reform. The paper highlights the limitations of traditional teaching methods and proposes innovative solutions such as the use of digital music workstations and online platforms to improve teaching efficiency and student engagement. At the same time, the article advocates inclusiveness, advocating the integration of multicultural and modern music styles into the curriculum to cultivate students' global perspective. Through these reforms, we aim to build a more modern, interactive and inclusive music education system that opens students' doors to the world of music and stimulates their creativity and expression.
English Teaching Strategy Research and Effect Evaluation Based on Deep Learning in Intelligent Environment
In university English teaching, the integration of deep learning and wisdom classroom is very important to improve students' English thinking ability. The article first explains the connotation and correlation of deep learning and smart classroom. Then, it puts forward how to promote students' deep participation and thinking in college English teaching through the establishment of wisdom teaching objectives, the creation of teaching situations, the design of teaching activities and the development of teaching evaluation. It aims to build an efficient college English wisdom classroom and promote the development of students' comprehensive English ability. In order to test whether intelligent teaching environment and deep learning model can effectively improve the traditional college English teaching model: Students have low sense of learning, low ability of knowledge transfer and application, and low ability to deal with practical problems, improve teaching quality and efficiency, build college English in-depth classroom teaching mode based on intelligent teaching environment, and course teaching implementation process, and take a college English course as a sample to conduct a unit teaching quasi-experiment and effect evaluation.
Improved Differential Evolution Algorithm for 4PL Supply Chain Network Design Considering the Customer Behavior
Fourth party logistics (4PL) supply chain network design problem considering the behavior of customer service satisfaction becomes more important in supply chain network design problem. It is NP-hard problem. Improved differential evolution (IDE) algorithm is applied to solve this problem in this paper. In order to handle the infeasible solution efficiently, a two-stage algorithm, the minimum cost flow incorporated with macro-micro adjustment embedded improved differential evolution (MMAMFC-IDE), is designed. The results of numerical experiments show that MMAMFC-IDE can get relatively better results compared with the minimum cost flow embedded improved differential evolution(MFC-IDE).
Research and Practice on Classification Training Model of Academic Degree and Professional Degree for Computer Graduate Students
At present, it is a challenge to train professional and degree graduate students with advanced professional skills in computer science. It is an important way to improve the level of graduate students in computer science to establish the training mode of full-time professional graduate students. By analyzing the training characteristics of full-time computer master's degree and academic master's degree, this paper explores the training mode from the aspects of curriculum setting, practical training and dissertation, so as to provide reference for solving the confusion in training and promote the healthy development of full-time computer master's degree and academic degree education.
A Chinese View on Social Media and Its Implications on Cyber Security
Social media development creates new challenges to the sphere of cyberspace security and privacy. WeChat and Weibo have also been targeted by phishers. This analysis places particular emphasis on how social networks influence information systems protection in China, including the aspects of strategic insights on enterprises, social governance, information security, and national security. It addresses both, major depth of reach, such as AI-based adversary reconnaissance, as well as the weaknesses-such as the use of social networks for global intelligence capture. It also discusses China's separation and individuals with the encompassing methods of containment including legal, as well as modern methods, to counter the unique threats posed by an uncontrollable landscape of social networks.
Research on Image Fusion Based on Deep Adversarial Learning
Image fusion, as an important task in computer vision, essentially extracts important features from source images to complement each other and generate fusion images with higher quality and richer information. Infrared and visible images contain different information due to different imaging quantity principles. The key of infrared and visible image fusion algorithm is to integrate the thermal radiation information extracted from infrared images with the captured details and texture information of visible images, so as to obtain a fusion image with complete structure and rich detailed information. Based on the generative adversarial network model, this paper proposes an infrared and visible image fusion method based on dual path dual discriminator generating adversarial network, aiming at the problems existing in the existing research algorithms, such as inadequate extraction of feature information, low efficiency of network model feature transfer, easy loss of shallow information in single-path feature extraction, fewer fusion levels caused by sub-path feature extraction and unbalance of discriminator modes. The gradient path and contrast path based on the difference stitching of source images are constructed at the generator side to improve the detail information and contrast of fused images. The feature information of infrared and visible images is extracted by multi-scale decomposition to solve the problem of incomplete feature extraction on a single scale. Then the source image is introduced into each layer of the double-path dense network, which can improve the efficiency of feature transmission and obtain more source image information. At the end of the discriminator, a double discriminator is used to estimate the region distribution of infrared image and visible image, so as to avoid the mode imbalance problem of the loss of infrared image contrast information in the single discriminator network. Finally, we construct the master-auxiliary gradient and the master-auxiliary strength loss function to improve the information extraction ability of the network model. Compared with other image fusion methods on public data sets, the experimental results show that the proposed method achieves good results on objective evaluation indexes (mean gradient, spatial frequency, structural similarity and peak signal-to-noise ratio).
Key Technologies of Image Fusion Based on Deep Learning: A Survey
Image fusion is an important branch of image processing. Because of the remarkable advantages of neural network in image feature extraction and classification, the application of neural network technology in the field of image fusion is also a research hotspot in recent years. Firstly, the infrared and visible light image fusion algorithms based on shallow and deep neural networks are summarized, and the research progress of image fusion technology is introduced in detail, and the research results of fusion algorithms are presented. Finally, the challenges faced by image fusion are discussed, and the future development direction of this field is forecasted.
English Text Named Entity Recognition Method by Fusing Local and Global Features
Because of the ambiguity and dynamic nature of natural language, the research of named entity recognition is very challenging. As an international language, English plays an important role in the fields of science and technology, finance and business. Therefore, the early named entity recognition technology is mainly based on English, which is often used to identify the names of people, places and organizations in the text. International conferences in the field of natural language processing, such as CoNLL, MUC, and ACE, have identified named entity recognition as a specific evaluation task, and the relevant research uses evaluation corpus from English-language media organizations such as the Wall Street Journal, the New York Times, and Wikipedia. The research of named entity recognition on relevant data has achieved good results. Aiming at the sparse distribution of entities in text, a model combining local and global features is proposed. The model takes a single English character as input, and uses the local feature layer composed of local attention and convolution to process the text pieceby way of sliding window to construct the corresponding local features. In addition, the self-attention mechanism is used to generate the global features of the text to improve the recognition effect of the model on long sentences. Experiments on three data sets, Resume, MSRA and Weibo, show that the proposed method can effectively improve the model's recognition of English named entities.
Exploring Bioinspired AI for Advanced Cell Labeling: Advancements and Applications in Biomedical Research
Context: Bioinspired Artificial Intelligence (Bio-AI) has emerged as a transformative tool in biomedical research, addressing challenges in cell labeling essential for understanding cellular behavior and interactions. Traditional cell labeling methods often struggle with accuracy, scalability, and adaptability in complex datasets. Objective: This paper theoretically explores the integration of Bio-AI models into cell labeling process. It aims to assess how these models – encompassing neural networks, swarm intelligence, evolutionary algorithms, and self-organizing maps – can enhance cell identification and classification. Method: The paper examines various Bio-AI models that mimic biological processes such as neural functioning, swarm behavior, and evolutionary dynamics. It also evaluates the application of multimodal AI systems that combine imaging data with molecular and genetic information. In addition, the potential of dynamic cell labeling, inspired by neural plasticity, is discussed. Result: The integration of Bio-AI models has demonstrated significant improvements in accuracy, adaptability, and scalability for cell labeling. Automated labeling systems minimize human error and enhance reproducibility. Recent advancements in multimodal AI systems have shown promise in combining imaging with genetic and molecular data, providing more comprehensive insights into cellular behavior. Dynamic labeling models inspired by neural plasticity offer enhanced tracking of cellular transitions over time. Conclusion: Bio-AI holds transformative potential in biomedical research via enabling real-time, dynamic labeling, essential for tracking cellular changes over time in processes like cancer progression, stem cell differentiation, and immune response. The continued evolution of these AI-driven approaches is expected to accelerate breakthroughs in understanding diseases, tissue engineering, and regenerative medicine.
Research and Application of Multi-level Diverse Intelligent Algorithm Library Based on Artificial Intelligence Computing Platform
At present, artificial intelligence computing platforms are usually based on cloud hosts for services, which have the characteristics of fast training speed and a wide variety of model types. However, the online models of such platforms mostly adopt the form of downloading model files, which is difficult to integrate into traditional software system systems. In response to existing problems, this paper takes the relevant theoretical technologies of next-generation intelligent computing platforms as the development framework, and conducts research on the diversity of multi-level intelligent computing requirements, by implementing a universal algorithm model construction and automatic integration mechanism; Build a multi domain and multi-level application algorithm library for different application scenarios; Design a personalized algorithm recommendation based on knowledge reasoning and object-oriented approach, and build an emerging intelligent computing platform for analyzing and understanding real-world data, meeting the needs of complex engineering application software such as heavy backend, light frontend, loose coupling, microservices, etc., providing theoretical and technical support for innovative big data services and applications with diverse computing requirements.
Study on the Student Concentration in Class Based on Deep Multitasking Learning Framework
In order to solve the problem of poor teaching quality caused by classroom teachers' inability to grasp students' dynamics in time, this paper designs a feedback system for classroom attention with the help of the research on expression recognition technology in deep learning. In the real-time analysis of expression, although the deeper deep learning network has more accurate recognition effect, there are drawbacks of too large model and too many parameters in the network training process. In this paper, we propose a student concentration algorithm that uses the Convolutional Block attentional module (CBAM) and the Local Binary Pattern (LBP) to reduce the number of parameters in the model by replacing the convolution with the depth-separable convolution LBP preprocessing enhances the feature validity of the input feature map and improves the training speed and accuracy of the model. The experimental results show that the algorithm has a good discriminating effect on expression recognition, and the model is small.
A Review of Multi-modal Human Motion Recognition Based on Deep Learning
Human motion recognition is a research hotspot in the field of computer vision, which has a wide range of applications, including biometrics, intelligent surveillance and human-computer interaction. In vision-based human motion recognition, the main input modes are RGB, depth image and bone data. Each mode can capture some kind of information, which is likely to be complementary to other modes, for example, some modes capture global information while others capture local details of an action. Intuitively speaking, the fusion of multiple modal data can improve the recognition accuracy. In addition, how to correctly model and utilize spatiotemporal information is one of the challenges facing human motion recognition. Aiming at the feature extraction methods involved in human action recognition tasks in video, this paper summarizes the traditional manual feature extraction methods from the aspects of global feature extraction and local feature extraction, and introduces the commonly used feature learning models of feature extraction methods based on deep learning in detail. This paper summarizes the opportunities and challenges in the field of motion recognition and looks forward to the possible research directions in the future.
Big Data Clustering Optimization Based on Intuitionistic Fuzzy Set Distance and Particle Swarm Optimization for Wireless Sensor Networks
Big data clustering plays an important role in the field of data processing in wireless sensor networks. However, there are some problems such as poor clustering effect and low Jaccard coefficient. This paper proposes a novel big data clustering optimization method based on intuitionistic fuzzy set distance and particle swarm optimization for wireless sensor networks. This method combines principal component analysis method and information entropy dimensionality reduction to process big data and reduce the time required for data clustering. A new distance measurement method of intuitionistic fuzzy sets is defined, which not only considers membership and non-membership information, but also considers the allocation of hesitancy to membership and non-membership, thereby indirectly introducing hesitancy into intuitionistic fuzzy set distance. The intuitionistic fuzzy kernel clustering algorithm is used to cluster big data, and particle swarm optimization is introduced to optimize the intuitionistic fuzzy kernel clustering method. The optimized algorithm is used to obtain the optimization results of wireless sensor network big data clustering, and the big data clustering is realized. Simulation results show that the proposed method has good clustering effect by comparing with other state-of-the-art clustering methods.
The Teaching Reform of "Automatic Control Theory" Course
Modern control theory course is a compulsory course for automation majors. It is based on automatic control theory course and plays an important role in teaching process. In view of the problems in the teaching of modern control theory, such as abstract content and weak mathematical foundation, this paper puts forward some reform suggestions from the aspects of teaching method, mathematical foundation, and the difference and connection with the course of automatic control theory, in order to improve students' learning interest and teaching quality. Based on the analysis of the teaching status of automatic control theory in School of Electronic and Electrical Engineering of Zhengzhou University of Science and Technology, combined with the reform needs of ideological and political needs, teaching methods, assessment and evaluation, this paper puts forward a multi-dimensional curriculum reform with "moral education as the center" and "case type" + "online and offline hybrid" + "ideological and political" Thinking. Some results have been obtained through practice.
Coupling Relation Strength with Graph Convolutional Networks for Knowledge Graph Completion
In the link prediction task of knowledge graph completion, Graph Neural Network (GNN)-based knowledge graph completion models have been shown by previous studies to produce large improvements in prediction results. However, many of the previous efforts were limited to aggregating the information given by neighboring nodes and did not take advantage of the information provided by the edges represented by relations. To address the problem, Coupling Relation Strength with Graph Convolutional Networks (RS-GCN) is proposed, which is a model with an encoder-decoder framework to realize the embedding of entities and relations in the vector space. On the encoder side, RS-GCN captures graph structure and neighborhood information while aggregating the information given by neighboring nodes. On the decoder side, RotatE is utilized to model and infer various relational patterns. The models are evaluated on standard FB15k, WN18, FB15k-237 and WN18RR datasets, and the experiments show that RS-GCN achieves better results than the current state-of-the-art classical models on the above knowledge graph datasets.
English Text Sentiment Analysis Based on Convolutional Neural Network and U-network
English text sentiment orientation analysis is a fundamental problem in the field of natural language processing. The traditional word segmentation method can produce ambiguity when dealing with English text. Therefore, this paper proposes a novel English text sentiment analysis based on convolutional neural network and U-network. The proposed method uses a parallel convolution layer to learn the associations and combinations between word vectors. The results are then input into the hierarchical attention network whose basic unit is U-network to determine the affective tendency. The experimental results show that the accuracy of bias classification on the English review dataset reaches 93.45%. Compared with many existing sentiment analysis models, it has more accuracy.
English-Chinese Neural Machine Translation Based on Self-organizing Mapping Neural Network and Deep Feature Matching
The traditional Chinese-English translation model tends to translate some source words repeatedly, while mistakenly ignoring some words. Therefore, we propose a novel English-Chinese neural machine translation based on self-organizing mapping neural network and deep feature matching. In this model, word vector, two-way LSTM, 2D neural network and other deep learning models are used to extract the semantic matching features of question-answer pairs. Self-organizing mapping (SOM) is used to classify and identify the sentence feature. The attention mechanism-based neural machine translation model is taken as the baseline system. The experimental results show that this framework significantly improves the adequacy of English-Chinese machine translation and achieves better results than the traditional attention mechanism-based English-Chinese machine translation model.
Data Visualization Analysis Based on Explainable Artificial Intelligence: A Survey
With the rapid development of computer hardware and big data processing technology, the bottleneck of intelligent analysis of massive data has changed from "how to deal with massive data quickly" to "how to mine valuable information quickly and effectively from massive data". Visualization and visualization analysis based on human visual perception characteristics, combined with data analysis and human-computer interaction and other technologies, use visual charts to deconstruct the knowledge and rules contained in complex data. This technology runs through the whole life cycle of data science, known as the last kilometer in the field of big data intelligence, and has achieved remarkable results in many big data application analysis scenarios. Traditional visualization analysis is extremely dependent on the user's frequent active participation in the whole life cycle of visualization analysis, including data preparation, data conversion, visualization mapping, visual rendering, user interaction, visual analysis and other stages, which require high professional skills of users and low intelligence of the system. Therefore, the traditional visualization analysis mode and systems have the challenges of high threshold of visualization analysis, high cost of data preparation, high latency of interaction response, and low efficiency of interaction mode. Therefore, this paper introduces the application, challenges in visualization based on explainable AI.
Constructing a Specialized Computer Integrated Circuit Reliability Experiment Platform
Against the backdrop of the strategy to build a manufacturing powerhouse and the rapid development of integrated circuits (ICs), the emergence of new technologies, materials, and processes has rendered traditional IC reliability techniques inadequate for the evolving needs of IC development. Addressing the construction of an innovative IC reliability experiment platform has become a critical issue for scientific researchers at our city's universities. This paper takes the Shenyang branch of the National Special Computer Research Center as a case study to explore how to optimize the construction of an IC reliability innovation platform. The discussion is structured around three dimensions: expanding the platform's application scope, enhancing talent capabilities, and improving technological innovation levels. The findings are instrumental in elevating the standard of innovation platforms within the city.
Building Metaverse Digital Humanities Labs: Strategies and Challenges in Higher Education
The integration of metaverse technologies within the academic sphere is redefining the landscape of digital humanities research and education. This study examines the current state and future prospects of metaverse digital humanities laboratories in higher education institutions, highlighting their importance and the challenges they face. The research underscores the significance of these laboratories in fostering interdisciplinary collaboration, enhancing educational experiences, and promoting cultural preservation through digital means. The metaverse digital humanities laboratories are seen as crucial platforms for innovation, providing immersive environments that facilitate the application of advanced digital tools in humanities research. The study identifies key challenges, including the need for robust hardware infrastructure, the complexity of technology integration, data management concerns, and the necessity for ethical frameworks that address privacy and intellectual property rights. It also emphasizes the importance of user experience, interdisciplinary collaboration, and the development of a comprehensive talent cultivation system that aligns with the digital humanities. Strategies for the construction and development of these laboratories are proposed, such as accelerating metaverse infrastructure development, promoting the transformation of traditional digital humanities laboratories, and establishing mechanisms for data protection and resource sharing. The research also calls for the enhancement of user experiences through personalized services and the cultivation of talent through interdisciplinary educational programs. In conclusion, the metaverse digital humanities laboratories represent a strategic frontier in academic innovation, offering transformative potential for research and education in the humanities. The study provides a roadmap for the development of these laboratories, suggesting a comprehensive approach that addresses technological, pedagogical, ethical, and financial challenges, and emphasizing the importance of strategic planning and interdisciplinary collaboration in realizing their full potential.
Ideological and Political Exploration of Academic Continuing Education Professional Diploma in Medical imaging
The 20th National Congress of the Communist Party of China proposed "promoting the construction of a Healthy China and placing the safeguarding of people's health in a strategic position of priority development." Standing at a new era and starting point, medical services will inevitably serve the whole society with new attitudes and appearances. This poses higher requirements and greater challenges for the training of medical students and continuing medical education. In recent years, the concept of "ideological and political education within the curriculum" has gradually become clear, and ideological and political education runs through the entire process of talent training in universities. This article, combining the characteristics of medical imaging professional student training and continuing education, elaborates on the new model of "3+4+2" ideological and political education at first affiliated hospital of Dalian Medical University . It constructs a new system of "school-family-society" tripartite co-education, establishes a new mechanism of "party committee-functional departments-teaching and research office-teachers" four-level linkage, and explores a new approach of "party branch-teaching and research office" dual-wheel drive, integrating value shaping, knowledge imparting, and ability cultivation. It aims to cultivate talents for the Party and educate people for the country, effectively enhance the effectiveness of "comprehensive education", and provide strong support for the construction of new medical sciences.
A Novel Density-Based Spatial Clustering of Application with Noise Method for Data Clustering
The traditional methods are easy to generate a large number of fake samples or data loss when classifying unbalanced data. Therefore, this paper proposes a novel DBSCAN (density-based spatial clustering of application with noise) for data clustering. The density-based DBSCAN clustering decomposition algorithm is applied to most classes of unbalanced data sets, which reduces the advantage of most class samples without data loss. The algorithm uses different distance measurements for disordered and ordered classification data, and assigns corresponding weights with average entropy. The experimental results show that the new algorithm has better clustering effect than other advanced clustering algorithms on both artificial and real data sets.
Navigating Biomedical Ethical Challenges of Artificial Intelligence in Healthcare
Biomedical ethics, commonly known as bioethics, constitutes a dynamic and interdisciplinary field that confronts the intricate ethical challenges and moral quandaries inherent at the convergence of medicine, healthcare, and the life sciences. Its foundation lies in the core principles of respect for autonomy, beneficence, non-maleficence, and justice, which collectively establish a robust framework for guiding ethical decision-making in the realm of clinical practice, research endeavors, and the formulation of healthcare policies. The principle of autonomy underscores the importance of respecting individuals' rights to make informed decisions about their own healthcare, while beneficence and non-maleficence emphasize the ethical obligation to promote well-being and prevent harm. Justice, in the context of biomedical ethics, demands the equitable distribution of resources and fair treatment for all individuals. Biomedical ethics serves as an indispensable compass for healthcare professionals, researchers, policymakers, and society at large, providing ethical guidance to navigate the intricate landscape of advancements in medicine and the life sciences.
Improved Long Short-term Memory Network for Gesture Recognition
Surface EMG contains a lot of physiological information reflecting the intention of human movement. Gesture recognition by surface EMG has been widely concerned in the field of human-computer interaction and rehabilitation. At present, most studies on gesture recognition based on surface EMG signal are obtained by discrete separation method, ignoring continuous natural motion. A gesture recognition method of surface EMG based on improved long short-term memory network is proposed. sEMG sensors are rationally arranged according to physiological structure and muscle function. In this paper, the finger curvature is used to describe the gesture state, and the gesture at every moment can be represented by the set of different finger curvature, so as to realize continuous gesture recognition. Finally, the proposed gesture recognition model is tested on Ninapro (a large gesture recognition database). The results show that the proposed method can effectively improve the representation mining ability of surface EMG signal, and provide reference for deep learning modeling of human gesture recognition.
Review for Anomaly Detection in Video Surveillance System Based on Deep Learning
In this paper, abnormal target detection and location in video surveillance system are studied. In recent years, with the rapid development of network information technology, video surveillance technology has been widely used, artificial anomaly detection methods have no way to meet the effective growth of video surveillance data, with 3D technology, face recognition technology, etc., also promote the development of the field of computer vision, for the rapid analysis of a large number of video data to provide effective support. At present, abnormal target detection methods in video surveillance system mainly include the following two methods: One is to extract two-dimensional data features from video surveillance data, and effectively express video targets according to the extracted features. The information expressed mainly includes time information and spatial information. The second is to directly learn 3D space-time features for the module with motion information to detect the location of the abnormal target. Finally, the paper summarizes the full text and looks forward to the future development direction of video anomaly detection from three aspects: data set, method and evaluation index.