Video Recommendation System Using Machine-Learning Techniques

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through learning.In this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different clients.The expectation depends on past evaluations,history,interest,IMDB rating,and so on.This can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific time.The required datasets for the video are taken from Grouplens.This recommender framework is executed by utilizing Python Programming Language.For building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the examples.For that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information focuses.The last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

The AI Paradox: Mapping the Unintended Disruptions in IT and Beyond

In the realm of Artificial Intelligence (AI), there exists a complex landscape where promises of efficiency and innovation clash with unforeseen disruptions across Information Technology (IT) and broader societal realms. This paper sets out on a journey to explore the intricate paradoxes inherent in AI, focusing on the unintended consequences that ripple through IT and beyond. Through a thorough examination of literature and analysis of related works, this study aims to shed light on the complexities surrounding the AI paradox. It delves into how this paradox appears in various domains, such as algorithmic biases, job displacement, ethical dilemmas, and privacy concerns. By mapping out these unintended disruptions, this research seeks to offer a nuanced understanding of the challenges brought forth by AI-driven transformations. Ultimately, its goal is to pave the way for the responsible development and deployment of AI, fostering a harmonious integration of technological progress with societal values and priorities.

Ethical framework for artificial intelligence in healthcare research:A path to integrity

The integration of Artificial Intelligence(AI)into healthcare research promises unprecedented advancements in medical diagnostics,treatment personalization,and patient care management.However,these innovations also bring forth significant ethical challenges that must be addressed to maintain public trust,ensure patient safety,and uphold data integrity.This article sets out to introduce a detailed framework designed to steer governance and offer a systematic method for assuring that AI applications in healthcare research are developed and executed with integrity and adherence to medical research ethics.

Artificial intelligence ecosystem for computational psychiatry:Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms.One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable.This may help researchers develop more effective treatments and interventions for mental health problems.This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry.The artificial intelligence ecosystem for computational psychiatry includes data acquisition,preparation,modeling,application,and evaluation.This approach allows researchers to integrate data from a variety of sources,such as brain imaging,genetics,and behavioral experiments,to obtain a more complete understanding of mental health conditions.Through the process of data preprocessing,training,and testing,the data that are required for model building can be prepared.By using machine learning,neural networks,artificial intelligence,and other methods,researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors.Despite the continuous development and breakthrough of computational psychiatry,it has not yet influenced routine clinical practice and still faces many challenges,such as data availability and quality,biological risks,equity,and data protection.As we move progress in this field,it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

Using of artificial intelligence:Current and future applications in colorectal cancer screening

Significant developments in colorectal cancer screening are underway and include new screening guidelines that incorporate considerations for patients aged 45 years,with unique features and new techniques at the forefront of screening.One of these new techniques is artificial intelligence which can increase adenoma detection rate and reduce the prevalence of colonic neoplasia.

On recommendation-aware content caching for 6G:An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.

Present Status of Clinical Decision Support System （CDSS） and Proposal of Ethical Guideline for Artificial Intelligence Development in Medical Field

This paper is looking at the trends of future medicine focusing on IBM Watson, Microsoft Intelligent Network for Eye care （MINE）, and Google’s AI Eye Doctor. Microsoft’s Mine, an artificial intelligence assistant program, and Google’s Eye Doctor, were created because of the two characteristics of eye disease. The disease of the eye significantly reduces the quality of human life, but it can be prevented if the illness is discovered in advance. Therefore, prevention of these diseases is important. Through the development of the Google eye Doctor Program shown in the article of “Retinopathy Algorithm”, we obtained the implications for the ethical guidelines that can be applied to other artificial intelligence development programs. At the heart of these guidelines is the efficient and safe treatment of patient illnesses for medical purposes.

Ethical Analysis of Ethical Issues and Countermeasures in Education in the Era of Artificial Intelligence

The integration of artificial intelligence(AI)in education has revolutionized teaching and learning methodologies,offering personalized experiences and efficient resource management.However,this technological advancement has also surfaced a plethora of ethical concerns that necessitate careful consideration.This paper delves into the ethical issues arising from AI applications in education,such as data privacy,algorithmic bias,educational equity,and the evolving role of teachers.Through a comprehensive analysis,we identify the challenges and propose strategic countermeasures to mitigate these ethical dilemmas.Case studies from both domestic and international contexts are employed to illustrate real-world applications and the associated ethical decision-making processes.The paper concludes with a summary of findings,policy recommendations,and an outlook on future research directions,emphasizing the need for a balanced approach that respects both technological innovation and ethical standards in educational AI deployment.

Artificial intelligence in sleep medicine:Present and future

Artificial intelligence(AI)has impacted many areas of healthcare.AI in healthcare uses machine learning,deep learning,and natural language processing to analyze copious amounts of healthcare data and yield valuable outcomes.In the sleep medicine field,a large amount of physiological data is gathered compared to other branches of medicine.This field is primed for innovations with the help of AI.A good quality of sleep is crucial for optimal health.About one billion people are estimated to have obstructive sleep apnea worldwide,but it is difficult to diagnose and treat all the people with limited resources.Sleep apnea is one of the major contributors to poor health.Most of the sleep apnea patients remain undiagnosed.Those diagnosed with sleep apnea have difficulty getting it optimally treated due to several factors,and AI can help in this situation.AI can also help in the diagnosis and management of other sleep disorders such as insomnia,hypersomnia,parasomnia,narcolepsy,shift work sleep disorders,periodic leg movement disorders,etc.In this manuscript,we aim to address three critical issues about the use of AI in sleep medicine:(1)How can AI help in diagnosing and treating sleep disorders?(2)How can AI fill the gap in the care of sleep disorders?and(3)What are the ethical and legal considerations of using AI in sleep medicine?

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

New AI-IP-EI Trilogy Opens Innovation to New Dimensions;Another Chip in “the Innovation Wall”, What About Emotional Intelligence (EI)?

The red thread of the AI-IP-EI Trilogy fate of this study, may have the appearance of a pot-pourri of intellectual and intelligence natures, as a matter of fact that it emanates from the genesis and practical synergism of the trilogy components. Concretely: The paper goes from: AI (Artificial Intelligence)—to the related IP (Intellectual Property) domain—to the relevance of EI (Emotional Intelligence);thus, forming the new AI-IP-EI Trilogy and its attributes and specific impacts to the new innovation process, and business model dimensions. These impacts are outlined and illustrated in part in essays of specific sections and all along. Several concrete study cases are used in the various dedicated sections;such as cases respective to the inventor status, and the EI factor, to the sport education innovative dimension, as well as to biases as inevitably promoting and revealing, to drastically enlarge open innovation supported by constructivism and creations of musical group as a model of open reflexive education. Overall resulting adapted business models appear to have a massive potential, and a multidimensional reach with a necessary attention to the IP policy on going definition. The durable green dimension is exemplified as well. The Ethics-plus, “@LEAST?”, said corpus is proposed. Could a human centric, AI-adapted-IP policy, internationally embraced, take part to some level of arbitrage, normative and enduring reliability in the field of interest? This seems to be “en route”. Shall the EI (Emotional Intelligence) factor be supervised? Likely so. Is traditional open innovation renewed to a more comprehensive, more inclusive dimension reminding best business practice and now “beyond”? Definitely, and will remain an opportunity, all along the 4IR quantum game changer to come. Neither seeking an in-depth expert analysis, nor a grand public over-simplified bavardage, of the trilogy, AI-IP-EI, four authors here propose an illustrated view of scientific, educational, visionary, demonstrative value to the subject matter. They are aged about 30-40-50-60, being IP & Innovation strategist, future IP lawyer, children-teacher and professional academy sport coach, illustrator and bio-advanced materials engineering “Fellow Scientist”. With experience of large and smaller organizations, being involved innovators, inventors and private artists as well, they are sharing their “non-jargonized down-to-earth”, forward looking views through a structured analysis of the trilogy using realistic examples and data from rather diverse specialized independent sources, biotechnology, nanomaterials, sport… New invention and inventorship is been “reconceptualized” at least from an “insighter or insider” viewpoint, and sport team approach more broadly revisited from its academy level to its commercial asset impact, via educational virtues and values. Music group constructivism enters the scene as well with its exemplary reflexivity and alterity valued for open innovation. Science is the prime lead. “Emotional intelligence, EI, is still an emerging area within AI” and beyond? A new open innovation scheme is taking place. This prompted our intention to further contribute to this matter. Is EI, the tree gently challenging the wind? Generated by AI and IP streams and scientific applications therewith? Naturally. Conclusions are encouraging the follow-up of promising orientations underlined by the AI-IP-EI Trilogy, favoring human centric feature adoptions.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

Enhancing Cybersecurity through AI and ML: Strategies, Challenges, and Future Directions

The landscape of cybersecurity is rapidly evolving due to the advancement and integration of Artificial Intelligence (AI) and Machine Learning (ML). This paper explores the crucial role of AI and ML in enhancing cybersecurity defenses against increasingly sophisticated cyber threats, while also highlighting the new vulnerabilities introduced by these technologies. Through a comprehensive analysis that includes historical trends, technological evaluations, and predictive modeling, the dual-edged nature of AI and ML in cybersecurity is examined. Significant challenges such as data privacy, continuous training of AI models, manipulation risks, and ethical concerns are addressed. The paper emphasizes a balanced approach that leverages technological innovation alongside rigorous ethical standards and robust cybersecurity practices. This approach facilitates collaboration among various stakeholders to develop guidelines that ensure responsible and effective use of AI in cybersecurity, aiming to enhance system integrity and privacy without compromising security.

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.

Artificial Intelligence Trends and Ethics: Issues and Alternatives for Investors

Artificial intelligence (AI) based technology, machine learning, and cognitive systems have played a very active role in society’s economic and technological transformation. For industrial value chains and international businesses, it means that a structural change is necessary since these machines can learn and apply new information in making forecasts, processing, and interacting with people. Artificial intelligence (AI) is a science that uses powerful enough techniques, strategies, and mathematical modelling to tackle complex actual problems. Because of its inevitable progress further into the future, there have been considerable safety and ethical concerns. Creating an environment that is AI friendly for the people and vice versa might be a solution for humans and machines to discover a common set of values. In this context, the goal of this study is to investigate the emerging trends of AI (the benefits that it brings to the society), the moral challenges that come from ethical algorithms, learned or pre-set ideals, as well as address the ethical issues and malpractices of AI and AI security. This paper will address the consequences of AI in relation to investors and financial services. The article will examine the challenges and possible alternatives for resolving the potential unethical issues in finance and will propose the necessity of new AI governance mechanisms to protect the efficiency of the capital markets as well as the role of financial authority in the regulation and monitoring of the huge expansion of AI in finance.

Reflection on the equitable attribution of responsibility for artificial intelligence-assisted diagnosis and treatment decisions

Artificial intelligence(AI)is developing rapidly and is being used in several medical capacities,including assisting in diagnosis and treatment decisions.As a result,this raises the conceptual and practical problem of how to distribute responsibility when AI-assisted diagnosis and treatment have been used and patients are harmed in the process.Regulations on this issue have not yet been established.It would be beneficial to tackle responsibility attribution prior to the development of biomedical AI technologies and ethical guidelines.In general,human doctors acting as superiors need to bear responsibility for their clinical decisions.However,human doctors should not bear responsibility for the behavior of an AI doctor that is practicing medicine inde-pendently.According to the degree of fault-which includes internal institutional ethics,the AI bidding process in procurement,and the medical process-clinical institutions are required to bear corresponding responsibility.AI manufacturers are responsible for creating accurate algorithms,network security,and insuring patient privacy protection.However,the AI itself should not be subjected to legal evaluation since there is no need for it to bear responsibility.Corresponding responsibility should be borne by the employer,in this case the medical institution.

Artificial Intelligence in Healthcare and Medicine:Promises,Ethical Challenges and Governance

Artificial intelligence (AI) is rapidly being applied to a wide range of fields,including medicine,and has been considered as an approach that may augment or substitute human professionals in primary healthcare.However,AI also raises several challenges and ethical concerns.In this article,the author investigates and discusses three aspects of AI in medicine and healthcare:the application and promises of AI,special ethical concerns pertaining to AI in some frontier fields,and suggestive ethical governance systems.Despite great potentials of frontier AI research and development in the field of medical care,the ethical challenges induced by its applications has put forward new requirements for governance.To ensure “trustworthy” AI applications in healthcare and medicine,the creation of an ethical global governance framework and system as well as special guidelines for frontier AI applications in medicine are suggested.The most important aspects include the roles of governments in ethical auditing and the responsibilities of stakeholders in the ethical governance system.

The Limitations and Ethical Considerations of ChatGPT

With the advancements of artificial intelligence technology,ChatGPT,a new practice of artificial intelligence,holds immense potential across multiple fields.Its user-friendly human-machine interface,rapid response capabilities,and delivery of high-quality answers have attracted considerable attention and widespread usage.Regarded by many as a groundbreaking advancement in AI,ChatGPT represents a new milestone in the field.However,as with any technological evolution,the emergence of ChatGPT brings not only benefits,but also inevitable security risks and ethical issues.This paper provides specific information about ChatGPT,including its technology,limitations,ethical issues,governance paths and future directions.Specifically,we firstly offered a thorough exploration of the technical implementation details of GPT series models.Next,we provided an intricate analysis elucidating the reasons for limitations and scrutinized the consequential impacts,such as malicious misuse,privacy violation,and so on.Finally,we explore diverse governance paths to mitigate the impacts of ChatGPT and present future directions.This review aims to equip users with crucial knowledge,facilitating well-informed decision-making,effectively handling of potential challenges in employing ChatGPT,and staying abreast with the rapidly evolving landscape of this technology.

Artificial intelligence-assisted psychosis risk screening in adolescents:Practices and challenges

Artificial intelligence-based technologies are gradually being applied to psychiatric research and practice.This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents.In terms of the practice of psychosis risk screening,the application of two artificial intelligence-assisted screening methods,chatbot and large-scale social media data analysis,is summarized in detail.Regarding the challenges of psychiatric risk screening,ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence,which must comply with the four biomedical ethical principles of respect for autonomy,nonmaleficence,beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings.By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens,we propose that assuming they meet ethical requirements,there are three directions worth considering in the future development of artificial intelligenceassisted psychosis risk screening in adolescents as follows:nonperceptual realtime artificial intelligence-assisted screening,further reducing the cost of artificial intelligence-assisted screening,and improving the ease of use of artificial intelligence-assisted screening techniques and tools.

智慧医疗场景下人工智能应用伦理问题与治理路径探讨

随着人工智能在医疗健康领域的广泛应用,医学伦理学与科技伦理挑战也随之而来。作者归纳了人工智能在医疗健康领域的应用现状,探讨了目前人工智能技术应用于医疗领域导致的医生主体地位被削弱、数据或隐私安全风险增加、医患信任危机加剧、医疗资源分配不均加剧、事故责任界限模糊等伦理问题,剖析了上述伦理问题产生的原因,最后从强化医生主体地位、建立健全信息数据保护制度、增强患者对医务人员的信任感、提高医疗资源配置效率、完善事故责任界定体系等5个方面提出治理路径,以期为人工智能技术更好地助力医疗发展提供参考。