Artificial Intelligence Learning Research Articles

Challenges and directions for digital twin implementation in otorhinolaryngology.

Digital twin technology heralds a transformative era in Otorhinolaryngology (ORL), merging the physical and digital worlds to offer dynamic, virtual models of physical entities or processes. These models, capable of simulating, predicting, and optimizing real-world counterparts, are evolving from static replicas to intelligent, adaptive systems. Fueled by advancements in communication, sensor technology, big data analytics, Internet of Things (IoT), and simulation technologies, artificial intelligence (AI), digital twins in ORL promise personalized treatment planning, virtual experimentation, and therapeutic intervention optimization. Despite their potential, the integration of digital twins in ORL faces challenges including data privacy and security, data integration and interoperability, computational demands, model validation and accuracy, ethical and regulatory considerations, patient engagement, and cost and accessibility issues. Overcoming these challenges requires robust data protection measures, seamless data integration, substantial computational resources, rigorous validation studies, ethical transparency, patient education, and making the technology accessible and affordable. Looking ahead, the future of digital twins in ORL is bright, with advancements in AI and machine learning, omics data integration, real-time monitoring, virtual clinical trials, patient empowerment, seamless healthcare integration, longitudinal data analysis, and collaborative research. These developments promise to refine diagnostic and treatment strategies, enhance patient care, and facilitate more efficient and tailored ORL research, ultimately leading to more effective and personalized ORL management.

Teaching and learning artificial intelligence: Insights from the literature

Teaching and learning artificial intelligence: Insights from the literature

Pattern Recognition of EFL University Students’ Online Behaviors through Data Science: Any Investment on English Language Components or Skills?

Artificial intelligence (AI) has played a crucial role in many fields of study, and its merits and demerits have been investigated by related scholars. However, it seems a somewhat gray area when implementing AI in teaching and learning. This could be mainly because AI developers know little about learning varied sciences and they also lack pedagogical knowledge for implementing AI in teaching (Luckin & Cukurova, 2019). Therefore, the current research aimed at gathering data regarding the online behaviors of 21 Iranian EFL university students through installing an application on their mobile phones for a period of eight days. Afterwards, the collected data were analyzed through Data Science methods and varied patterns of behavior were recognized. The results exhibited that messaging and social media applications are two great parts of the students’ life. Also, there existed some common patterns regarding the applications students owned and the amount of time they spent on them. As a small case in point, the results of the study cannot be generalized; however, the outcomes highlight the proof that in an EFL context, serious steps need to be adopted by course designers and stakeholders in order to cultivate a greater and purposeful use of online learning applications, websites etc. among EFL university students so as to enhance the teaching-learning process. This gains prominence because any AI-mediated learning approach is believed to enhance students’ second/foreign language motivation as well as self-regulation in learning. AI is actually promising a revolution in the realm of language education.

Securing Smart Cities: A Cybersecurity Perspective on Integrating IoT, AI, and Machine Learning for Digital Twin Creation

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

Advancements and challenges in AI-driven language technologies: From natural language processing to language acquisition

This paper explores the evolution and impact of artificial intelligence (AI) in the realm of language technologies. We trace the historical development of language models in AI, starting from the rule-based systems of the 1960s to the sophisticated neural networks of today. The current state-of-the-art technologies, particularly transformer-based models like OpenAI's GPT series, are examined for their capabilities and limitations. We delve into the role of AI in language acquisition and learning, highlighting AI-driven language teaching tools such as Duolingo and Babbel, and discuss their effectiveness and challenges. Furthermore, the paper explores the significant contributions of AI in second language acquisition research, including the development of predictive models and sophisticated learner profiles. Ethical considerations and challenges, such as data privacy and potential biases, are also addressed. We discuss advancements in natural language processing (NLP) applications like text and sentiment analysis, speech recognition and generation, and machine translation, along with their cross-linguistic challenges. The conclusion envisions future directions for AI in language technologies, emphasizing the need for multimodal inputs, efficiency, and enhanced interpretability.

Footprints of Artificial Intelligence in Climate Change

Abstract: Climate change and digital modifications are the two most dominant trends of our century and have become a global concern. The strategy in which humans can manage them, and their enlarged interactions will play a key role in mankind’s future. Primarily we have to make pathways to combine the climate and digital transformations in a way that validates our social and democratic values. In the present situation, climate change is the major concern that leads to the depletion of various environmental resources which is an alarming situation for living beings. Technology has changed a lot in the past few decades, changing our lives and us along with it. Artificial Intelligence is already playing a key role in Education, Health, and various other industries. Artificial intelligence (AI) is bringing about many changes in our perception of technology and lifestyle. Advancements in AI and Machine Learning (ML) are promising, and their applications are continually redefining the way we interact with our devices, people, and the planet. Various initiatives of Artificial Intelligence for tackling climate change have started, like the UN Climate Change Initiative on Artificial Intelligence for Climate Action. These investigate how AI might be used as a potent instrument to advance and intensify climate action that is revolutionary in poor nations. This paper mainly identifies the effective impacts of Artificial Intelligence on climate change and its sustainability.

Secure Cloud Services for the Healthcare Industry: Addressing Unique Challenges and Ensuring Compliance

Abstract: The healthcare industry is increasingly adopting cloud computing to manage patient data, provide telehealth services, and support remote work. However, this adoption presents unique challenges due to stringent regulations governing the handling of personal health information (PHI), such as HIPAA in the United States and GDPR in the European Union. Noncompliance with these regulations can result in significant financial penalties, reputational damage, and loss of patient trust. This article explores the specific data security needs of the healthcare industry, the challenges in adopting cloud services, and the essential features of secure cloud services tailored for healthcare. It also discusses the integration of artificial intelligence (AI) and automation in cloud services to enhance data security and operational efficiency, as well as future trends in cloud computing for healthcare, such as the increasing adoption of AI and machine learning for predictive analytics and automated threat detection. By providing a comprehensive overview of the data security needs, challenges, and solutions in adopting cloud services for healthcare, this article aims to assist healthcare organizations in making informed decisions when considering the adoption of cloud services and to highlight the importance of prioritizing data security and compliance in the digital transformation of the healthcare industry.

Realizing the Promise of Artificial Intelligence in Hepatocellular Carcinoma through Opportunities and Recommendations for Responsible Translation

This study aims to provide an overview of the current state-of-the-art applications of artificial intelligence (AI) and machine learning in the management of hepatocellular carcinoma (HCC), and to explore future directions for continued progress in this emerging field.  This study is a comprehensive literature review that synthesizes recent findings and advancements in the application of AI and machine learning techniques across various aspects of HCC care, including screening and early detection, diagnosis and staging, prognostic modeling, treatment planning, interventional guidance, and monitoring of treatment response. The review draws upon a wide range of published research studies, focusing on the integration of AI and machine learning with diverse data sources, such as medical imaging, clinical data, genomics, and other multimodal information.  The results demonstrate that AI-based systems have shown promise in improving the accuracy and efficiency of HCC screening, diagnosis, and tumor characterization compared to traditional methods. Machine learning models integrating clinical, imaging, and genomic data have outperformed conventional staging systems in predicting survival and recurrence risk. AI-based recommendation systems have the potential to optimize personalized therapy selection, while augmented reality techniques can guide interventional procedures in real-time. Moreover, longitudinal application of AI may enhance the assessment of treatment response and recurrence monitoring. Despite these promising findings, the review highlights the need for rigorous multicenter prospective validation studies, standardized multimodal datasets, and thoughtful consideration of ethical implications before widespread clinical implementation of AI technologies in HCC management.

AI and machine learning in medical imaging: key points from development to translation

Abstract Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

Harnessing AI for Education 4.0: Drivers of Personalized Learning

Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.

Analyzing the Possibilities of Implementation of AI and Social Networks in Teaching Foreign Language Students: Ukrainian Universities Case Study

The growing integration of artificial intelligence (AI) and social networks into the educational landscape has raised questions about their impact on teaching and learning, particularly in foreign language instruction. This study explores the didactic properties of social networks and AI as information and communication technology (ICT) tools in the context of foreign language education at the university level. Through a comprehensive literature review, the researchers analyzed global studies on the integration of AI and social networks in language learning, as well as research specific to the Ukrainian higher education context. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was conducted to systematically evaluate the potential benefits and challenges associated with the implementation of these technologies in foreign language instruction. The findings indicate that using AI-driven language learning applications, and social media platforms can significantly enhance vocabulary retention, language proficiency, and student engagement. However, the study also highlights potential threats, such as risks to academic integrity, entrenchment of discrimination, and the democratization of plagiarism. The research further emphasizes the importance of addressing teachers’ skills in navigating the ethical implications of AI integration in educational settings. The study provides valuable insights for university administrators, language instructors, and educational technology researchers. It underscores the need for a nuanced and informed approach to incorporating AI and social networks into foreign language curricula, balancing the pedagogical benefits with appropriate safeguards to ensure the quality and integrity of the educational process. The findings contribute to the evolving discourse on the role of emerging technologies in transforming higher education and language teaching.

A study on behavioral intentions of artificial intelligence learning platform: comparing the perspectives of teachers and students

ABSTRACT Artificial Intelligence Learning Platform (AILP) plays a key role in AI education. However, there are few studies investigating the behavioral intentions of teachers and students on using AILP and the sample size is small. This study examines factors affecting the behavioral intentions of 299 teachers and 347 students from China on using AILP. “Perceived Playfulness” is integrated into the Unified Theory of Acceptance and Use of Technology (UTAUT) model as the theoretical framework. Moreover, this study analyzes the moderating effects of gender, age, experience, voluntariness of use, teachers’ teaching level, teaching experience, and students’ major. The results of research through two structural equation models show that: (1) Students focus more on performance expectancy, whereas teachers are more concerned with perceived playfulness. (2) Students are easily affected by social influence, while teachers are not. (3) Both teachers and students are impacted by effort expectancy and facilitating conditions. This research provides a scientific comparison of affecting factors about behavioral intentions on using AILP between teachers and students, which can be used by researchers and AILP designers to optimize AILP design for better AI education.

Beyond the hype: exploring faculty perceptions and acceptability of AI in teaching practices

Limited studies exist on faculty members or lecturers’ perception and behavioural acceptance of artificial intelligence (AI) (e.g. ChatGPT) for their students' benefit. Teachers are the decision-makers for students' teaching and classroom activities. In this regard, the study examined lecturers’ use of AI-powered tools and the factors that influence the acceptance of AI in teaching and learning in universities. An online survey was conducted using a cross-sectional design, and the results were analysed using SPSS and SmartPLS. The study findings revealed that more than two-thirds (84%) of lecturers are willing to accept AI for their students, while 16% stated non-acceptance of AI for students. Factors such as years of teaching experience, institutional support for AI use, and attitude towards AI proved to be significant predictors of AI acceptance in education. Key factors influencing lecturers' acceptance of AI for their students include perceived pedagogical affordances, organisational policies and incentives, perceived complexity and usability and socio-cultural context. By addressing teacher concerns through supportive policies, user-friendly interfaces, and alignment with pedagogical goals, higher education institutions can create a more fertile ground for AI adoption.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

Analyzing the Impact of COVID-19 on Short-Term Investment Behavior through Stochastic Oscillator Indicators

Abstract This research examines how the COVID-19 pandemic affects short-term investments, particularly focusing on the reliability of Stochastic Oscillator Indicators (SOI) used by investors. Utilizing an artificial intelligence learning prediction model, the study investigates the pandemic's impact on short-term investment strategies guided by SOI, comparing data from pre-pandemic and pandemic periods. The findings suggest that during the COVID-19 period, employing KD analysis with a nine-round turnover rate leads to higher forecast accuracy. Short-term investors tend to adopt a conservative approach, with higher turnover rates resulting in lower winning rates. Overall, the study challenges the notion that COVID-19 only affects medium and long-term investment habits, shedding light on its influence on daily investment profits and potentially increasing short-term profits for larger, conservative investors. Further research is warranted to explore the investor profile of conservative hedgers in more depth. Keywords: COVID-19, Stochastic Oscillator Indicators, Artificial Intelligence, Short-term Investors, Conservative Approach

Exploring Factors That Support Pre-service Teachers’ Engagement in Learning Artificial Intelligence

AbstractArtificial intelligence (AI) is becoming increasingly relevant, and students need to understand the concept. To design an effective AI program for schools, we need to find ways to expose students to AI knowledge, provide AI learning opportunities, and create engaging AI experiences. However, there is a lack of trained teachers who can facilitate students’ AI learning, so we need to focus on developing the capacity of pre-service teachers to teach AI. Since engagement is known to enhance learning, it is necessary to explore how pre-service teachers engage in learning AI. This study aimed to investigate pre-service teachers’ engagement with learning AI after a 4-week AI program at a university. Thirty-five participants took part in the study and reported their perception of engagement with learning AI on a 7-factor scale. The factors assessed in the survey included engagement (cognitive—critical thinking and creativity, behavioral, and social), attitude towards AI, anxiety towards AI, AI readiness, self-transcendent goals, and confidence in learning AI. We used a structural equation modeling approach to test the relationships in our hypothesized model using SmartPLS 4.0. The results of our study supported all our hypotheses, with attitude, anxiety, readiness, self-transcendent goals, and confidence being found to influence engagement. We discuss our findings and consider their implications for practice and policy.

Prospective validation of a seizure diary forecasting falls short.

Recently, a deep learning artificial intelligence (AI) model forecasted seizure risk using retrospective seizure diaries with higher accuracy than random forecasts. The present study sought to prospectively evaluate the same algorithm. We recruited a prospective cohort of 46 people with epilepsy; 25 completed sufficient data entry for analysis (median = 5 months). We used the same AI method as in our prior study. Group-level and individual-level Brier Skill Scores (BSSs) compared random forecasts and simple moving average forecasts to the AI. The AI had an area under the receiver operating characteristic curve of .82. At the group level, the AI outperformed random forecasting (BSS = .53). At the individual level, AI outperformed random in 28% of cases. At the group and individual level, the moving average outperformed the AI. If pre-enrollment (nonverified) diaries (with presumed underreporting) were included, the AI significantly outperformed both comparators. Surveys showed most did not mind poor-quality LOW-RISK or HIGH-RISK forecasts, yet 91% wanted access to these forecasts. The previously developed AI forecasting tool did not outperform a very simple moving average forecasting in this prospective cohort, suggesting that the AI model should be replaced.

REVIEW OF INNOVATIVE APPROACHES TO MENTAL HEALTH TELETHERAPY: ACCESS AND EFFECTIVENESS

The field of mental health has witnessed a paradigm shift with the integration of teletherapy, offering innovative approaches to address accessibility and effectiveness in treatment. This review examines accessible teletherapy platforms, including mobile applications, web-based services, and immersive experiences facilitated by virtual reality (VR) and augmented reality (AR). A critical evaluation of these platforms highlights their usability, customizability, and potential to transcend traditional barriers to mental health services. It explores the integration of artificial intelligence (AI) for personalized interventions, gamification, and interactive techniques aimed at enhancing engagement, and the development of virtual support communities for peer interactions and shared experiences. Effectiveness is a central focus of the third section, with a comprehensive review of studies assessing the outcomes of innovative teletherapy approaches. Comparative analyses with traditional in-person therapy outcomes provide insights into evidence-based practices, while patient feedback and satisfaction surveys contribute to a nuanced understanding of the user experience. Challenges and considerations address technological barriers, ethical considerations, and regulatory implications associated with the integration of innovative teletherapy approaches. Recommendations for addressing these challenges underscore the need for a balanced and ethical implementation of teletherapy innovations. It explores potential future directions, including the integration of wearable technologies, advancements in AI and machine learning, and the importance of collaborative research initiatives. These future directions hold the promise of further transforming mental health teletherapy into a more personalized, accessible, and effective mode of treatment. This review consolidates findings on innovative approaches to mental health teletherapy, offering a comprehensive overview of platforms, therapeutic techniques, effectiveness, challenges, and future directions. The synthesis of these insights contributes to the ongoing dialogue surrounding the transformative potential of teletherapy in mental health, paving the way for continued advancements and improved mental health care delivery.
 Keywords:  Innovative, Mental, Health, Teletherapy, Access and Effectiveness.

Learning degradation-aware visual prompt for maritime image restoration under adverse weather conditions

Adverse weather conditions such as rain and haze often lead to a degradation in the quality of maritime images, which is crucial for activities like navigation, fishing, and search and rescue. Therefore, it is of great interest to develop an effective algorithm to recover high-quality maritime images under adverse weather conditions. This paper proposes a prompt-based learning method with degradation perception for maritime image restoration, which contains two key components: a restoration module and a prompting module. The former is employed for image restoration, whereas the latter encodes weather-related degradation-specific information to modulate the restoration module, enhancing the recovery process for improved results. Inspired by the recent trend of prompt learning in artificial intelligence, this paper adopts soft-prompt technology to generate learnable visual prompt parameters for better perceiving the degradation-conditioned cues. Extensive experimental results on several benchmarks show that our approach achieves superior restoration performance in maritime image dehazing and deraining tasks.