Harnessing the Power of Artificial Intelligence in Neuromuscular Disease Rehabilitation: A Comprehensive Review and Algorithmic Approach

Neuromuscular diseases present profound challenges to individuals and healthcare systems worldwide, profoundly impacting motor functions. This research provides a comprehensive exploration of how artificial intelligence (AI) technology is revolutionizing rehabilitation for individuals with neuromuscular disorders. Through an extensive review, this paper elucidates a wide array of AI-driven interventions spanning robotic-assisted therapy, virtual reality rehabilitation, and intricately tailored machine learning algorithms. The aim is to delve into the nuanced applications of AI, unlocking its transformative potential in optimizing personalized treatment plans for those grappling with the complexities of neuromuscular diseases. By examining the multifaceted intersection of AI and rehabilitation, this paper not only contributes to our understanding of cutting-edge advancements but also envisions a future where technological innovations play a pivotal role in alleviating the challenges posed by neuromuscular diseases. From employing neural-fuzzy adaptive controllers for precise trajectory tracking amidst uncertainties to utilizing machine learning algorithms for recognizing patient motor intentions and adapting training accordingly, this research encompasses a holistic approach towards harnessing AI for enhanced rehabilitation outcomes. By embracing the synergy between AI and rehabilitation, we pave the way for a future where individuals with neuromuscular disorders can access tailored, effective, and technologically-driven interventions to improve their quality of life and functional independence.

Innovation and Practice of Training Mode for Professional Postgraduates of Acupuncture and Tuina Based on Artificial Intelligence

In view of the common problems of integrating artificial intelligence into the training of postgraduates in Acupuncture and Tuina major,this paper reviews the related research progress both at home and abroad.It puts forward the innovative reform paths for integrating artificial intelligence into postgraduate training mode of Acupuncture and Tuina major:construct the teaching staff of artificial intelligence graduate students;innovating artificial intelligence to promote the integration of classics and scientific research;constructing the ideological and political case base of artificial intelligence courses;implementing artificial intelligence platform blended teaching;building a domestic and foreign exchange platform for artificial intelligence.Through practical research in teaching,it has achieved good teaching results and played a good demonstration,leading and radiation role in similar majors in China.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0.Specifically, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of processes.Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel accurately.The experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

The Industrial Internet of Things(IIoT)has brought numerous benefits,such as improved efficiency,smart analytics,and increased automation.However,it also exposes connected devices,users,applications,and data generated to cyber security threats that need to be addressed.This work investigates hybrid cyber threats(HCTs),which are now working on an entirely new level with the increasingly adopted IIoT.This work focuses on emerging methods to model,detect,and defend against hybrid cyber attacks using machine learning(ML)techniques.Specifically,a novel ML-based HCT modelling and analysis framework was proposed,in which L1 regularisation and Random Forest were used to cluster features and analyse the importance and impact of each feature in both individual threats and HCTs.A grey relation analysis-based model was employed to construct the correlation between IIoT components and different threats.

Relevance of sleep for wellness:New trends in using artificial intelligence and machine learning

Sleep and well-being have been intricately linked,and sleep hygiene is paramount for developing mental well-being and resilience.Although widespread,sleep disorders require elaborate polysomnography laboratory and patient-stay with sleep in unfamiliar environments.Current technologies have allowed various devices to diagnose sleep disorders at home.However,these devices are in various validation stages,with many already receiving approvals from competent authorities.This has captured vast patient-related physiologic data for advanced analytics using artificial intelligence through machine and deep learning applications.This is expected to be integrated with patients’Electronic Health Records and provide individualized prescriptive therapy for sleep disorders in the future.

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

Artificial intelligence for disease diagnostics still has a long way to go

Artificial intelligence(AI)can sometimes resolve difficulties that other advanced technologies and humans cannot.In medical diagnostics,AI has the advantage of processing figure recognition,especially for images with similar characteristics that are difficult to distinguish with the naked eye.However,the mechanisms of this advanced technique should be well-addressed to elucidate clinical issues.In this letter,regarding an original study presented by Takayama et al,we suggest that the authors should effectively illustrate the mechanism and detailed procedure that artificial intelligence techniques processing the acquired images,including the recognition of non-obvious difference between the normal parts and pathological ones,which were impossible to be distinguished by naked eyes,such as the basic constitutional elements of pixels and grayscale,special molecules or even some metal ions which involved into the diseases occurrence.

Advances in teleophthalmology and artificial intelligence for diabetic retinopathy screening:a narrative review

Background and Objective:Advances in teleophthalmology and artificial intelligence(AI)for diabetic retinal screening is of growing public health interest.Currently,only 30–40%of patients with diabetes adhere to recommended diabetes screening guidelines.To enhance early detection and reduce vision threatening complications,there has been a growing number of teleophthalmology programs and novel AI algorithms with the aim to improve eye care access.The purpose of this review is to assess current literature on teleophthalmology and AI for use in diabetic retinopathy(DR)screening,and to discuss advances and barriers to these innovative technologies.Methods:Literature review involving teleophthalmology and AI for DR screening,with focus on the past decade.Key Content and Findings:Teleophthalmology has demonstrated the ability to increase DR screening rates,enable earlier eye care access,and reduce healthcare costs.Novel AI-based DR screening programs appear accurate and effective,but detection of other ocular pathologies is still under development and not yet approved in the United States.Logistical,technological,financial,and legal barriers limit widespread adoption and long-term sustainability of teleophthalmology programs.Conclusions:The use of teleophthalmology and AI algorithms expands eye care access and helps prevent vision loss from DR and potentially other sight threatening conditions.Transparency in the process utilized for arriving at a particular diagnosis or decision to refer,often referred to as the“black box”,remains a multifaceted issue within the field of telemedicine for developing trust and improving patient-centered outcomes.

A Discussion of Artificial Intelligence in Visual Art Education

Since ChatGPT emerged on November 30, 2022, Artificial Intelligence (AI) has been increasingly discussed as a radical force that will change our world. People have become used to AI in which such ubiquitous technologies as Siri, Google, and Netflix deploy AI algorithms to answer questions, impart information, and provide recommendations. However, many individuals including originators and backers of AI have recently expressed grave concerns. In this paper, the authors will assess what is occurring with AI in Visual Arts Education, outline positives and negatives, and provide recommendations addressed specifically for teachers working in the field regarding emerging AI usage from kindergarten to grade twelve levels as well as in higher education.

Artificial Intelligence Adoption for Cybersecurity in Africa

Legacy-based threat detection systems have not been able to keep up with the exponential growth in scope, frequency, and effect of cybersecurity threats. Artificial intelligence is being used as a result to help with the issue. This paper’s primary goal is to examine how African nations are utilizing artificial intelligence to defend their infrastructure against cyberattacks. Artificial intelligence (AI) systems will make decisions that impact Africa’s future. The lack of technical expertise, the labor pool, financial resources, data limitations, uncertainty, lack of structured data, absence of government policies, ethics, user attitudes, insufficient investment in research and development, and the requirement for more adaptable and dynamic regulatory systems all pose obstacles to the adoption of AI technologies in Africa. The paper discusses how African countries are adopting artificial intelligence solutions for cybersecurity. And it shows the impact of AI to identify shadow data, monitor for abnormalities in data access and alert cyber security professionals about potential threats by anyone accessing the data or sensitive information saving valuable time in detecting and remediating issues in real-time. The study finds that 69.16% of African companies are implementing information security strategies and of these, 45% said they use technologies based on AI algorithms. This study finds that a large number of African businesses use tools that can track and analyze user behaviour in designated areas and spot anomalies, such as new users, strange IP addresses and login activity, changes to permissions on files, folders, and other resources, and the copying or erasure of massive amounts of data. Thus, we discover that just 18.18% of the target has no national cybersecurity strategy or policy. The study proposes using big data security analytics to integrate AI. Adopting it would be beneficial for all African nations, as it provides a range of cyberattack defense techniques.

Impact of Artificial Intelligence on Corporate Leadership

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling representations.The proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the system.Compared to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are minimized.Furthermore,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central servers.Therefore,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed method.Finally,four scenarios are chosen to investigate the projected design’s effectiveness.In all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

Toward Artificial General Intelligence: Deep Reinforcement Learning Method to AI in Medicine

Artificial general intelligence (AGI) is the ability of an artificial intelligence (AI) agent to solve somewhat-arbitrary tasks in somewhat-arbitrary environments. Despite being a long-standing goal in the field of AI, achieving AGI remains elusive. In this study, we empirically assessed the generalizability of AI agents by applying a deep reinforcement learning (DRL) approach to the medical domain. Our investigation involved examining how modifying the agent’s structure, task, and environment impacts its generality. Sample: An NIH chest X-ray dataset with 112,120 images and 15 medical conditions. We evaluated the agent’s performance on binary and multiclass classification tasks through a baseline model, a convolutional neural network model, a deep Q network model, and a proximal policy optimization model. Results: Our results suggest that DRL agents with the algorithmic flexibility to autonomously vary their macro/microstructures can generalize better across given tasks and environments.

Artificial Intelligence and the Sustainable Development Goals: An Exploratory Study in the Context of the Society Domain

Artificial Intelligence (AI) experienced significant advancements in recent years, and its potential power is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential influence of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather information on GPT-3’s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

A Deep Learning-Based Computational Algorithm for Identifying Damage Load Condition: An Artificial Intelligence Inverse Problem Solution for Failure Analysis

In this work,we have developed a novel machine(deep)learning computational framework to determine and identify damage loading parameters(conditions)for structures and materials based on the permanent or residual plastic deformation distribution or damage state of the structure.We have shown that the developed machine learning algorithm can accurately and(practically)uniquely identify both prior static as well as impact loading conditions in an inverse manner,based on the residual plastic strain and plastic deformation as forensic signatures.The paper presents the detailed machine learning algorithm,data acquisition and learning processes,and validation/verification examples.This development may have significant impacts on forensic material analysis and structure failure analysis,and it provides a powerful tool for material and structure forensic diagnosis,determination,and identification of damage loading conditions in accidental failure events,such as car crashes and infrastructure or building structure collapses.

AI赋能视角下的信息行为研究--2023年信息行为研究年会综述

2023年12月23—24日,2023年信息行为研究年会在北京大学召开,来自全国70多所高校的200余位知名专家学者和学生参加了会议。本次年会以“AI赋能视角下的信息行为研究”为主题,设置了主旨报告、专题讨论、海报展示、分论坛报告、眼动追踪工作坊等环节,围绕信息行为研究领域在新一代人工智能(AI)技术浪潮下的理论创新、方法应用、情境拓展、人本关怀、社会发展等方面进行深入研讨。文章综述了与会者的学术成果与主要观点,为信息行为研究的发展与创新提供理论启迪与研究指引。

基于AI的肺磨玻璃结节中医临床特点研究

目的通过客观、规范的方法分析肺磨玻璃中医临床特点,利用人工智能(AI)分析肺磨玻璃结节恶性风险,初步探究不同风险肺磨玻璃结节中医临床特点,为中医药干预肺结节提供参考。方法采集75例肺磨玻璃结节患者中医四诊信息,运用证素辨证方法提取证素;利用AI判读患者胸部CT,分析不同风险肺磨玻璃结节中医临床特点。结果肺磨玻璃结节患者中医证素以阴虚(77.33%)、气虚(56.00%)、阳虚(54.67%)、血虚(52.00%)等证素多见,亦可见痰(40.00%)、寒(37.33%)、气滞(32.00%)等实性证素;AI判读为高风险组较中低风险组气虚证素权值差异有统计学意义(P<0.05)。结论肺磨玻璃结节患者中医辨证以虚证为主,中医病机为气虚或气滞导致肺之气机不畅,引起局部痰、湿聚集于肺络而形成,而热证、瘀血证较少。

AI视域下图书馆视听资源智慧化加工的探索

新技术环境下网络视听服务蓬勃兴起,深刻影响和推动公共文化服务行业的变革与发展。视听资源是图书馆立体馆藏资源体系的重要组成部分,与图书馆传统的平面化文献资源相比,其立体化的资源呈现形式更具感染力、亲和力和传播力。论文探讨人工智能技术在图书馆视听资源的应用场景、实现功能,特别对人工智能技术在视听资源内容自动化识别、知识内容发现、知识体系构建、知识关联服务等方面的探索与应用进行了探讨,以期为其他机构在拓展智慧图书馆多维服务领域上开辟新的应用范围提供思路与参考。

AI对急诊外伤肋骨骨折诊断效能的应用研究

目的探讨CT不同重建算法图像对人工智能(AI)辅助肋骨骨折检测效能的影响及AI辅助诊断系统对放射科住院医师诊断急诊外伤肋骨骨折诊断效能的影响。方法收集急诊胸部外伤病史的患者50例,所有患者均利用256层螺旋CT进行胸部CT成像,使用Standard、Lung、Bone和Soft 4种不同kernel重建算法进行图像重建,计算并比较AI在不同重建算法胸部CT图像下对肋骨骨折识别灵敏度、特异度及准确度的差异。结果AI在Standard、Lung、Bone和Soft 4种算法下对骨折检出的灵敏度分别为75.74%、86.63%、89.60%和69.31%,Lung和Bone算法下AI对肋骨骨折检出的灵敏度高于Standard和Soft(P<0.05)。AI在Standard、Lung、Bone和Soft 4种算法下对骨折检出的特异度分别为93.88%、93.17%、93.78%和94.18%,4种算法比较差异无统计学意义(P>0.05)。AI在4种算法下的准确度分别为90.82%、92.07%,93.07%和89.98%,4种算法比较差异有统计学意义(P<0.05)。放射科住院医师不使用AI和使用AI诊断肋骨骨折的灵敏度分别为82.18%、96.53%,特异度分别为95.08%、94.78%,准确度分别为92.90%、95.08%,使用AI后对肋骨骨折诊断的灵敏度及准确度均明显提高(P<0.05),而特异度比较差异无统计学意义(P>0.05)。放射科住院医师不使用AI诊断所用的时间为(240.79±63.20)s,使用AI所用的时间为(105.26±57.20)s,两个时间比较差异有统计学意义(P<0.05)。结论放射科住院医师利用AI能够明显缩短急诊外伤肋骨骨折诊断所用时间,提高工作效率,而且能够提高其对于急诊外伤肋骨骨折诊断的准确性。AI在帮助放射科住院医师更准确、更快速识别肋骨骨折中具有重要的价值。