Artificial Intelligence Algorithms Research Articles

Consumer Protection and Deep Fakes - Assessing the Rights and Remedies for Victims in India

Abstract: In an era of fast technical improvement, the rise of deep fake technology poses a new and challenging problem for India's consumer protection. Deep fakes, a type of synthetic media developed with artificial intelligence algorithms, may modify audio, video, and images effectively, blurring the borders between truth and fabrication. Deep fakes can have far-reaching ramifications for consumers, including not only financial consequences but also social, psychological, and reputational harm. This topic's importance in the Indian setting cannot be emphasised. India, as a country with a diversified and large consumer base, is seeing an increase in digital contacts and transactions, making consumers increasingly vulnerable to the deceptive nature of deep fakes. Deep fakes pose a severe danger to consumer trust and confidence, ranging from intentionally manufactured marketing and counterfeit items to misinformation campaigns and identity theft.

AI-driven biometrics for secure fintech: Pioneering safety and trust

AI-driven biometrics is revolutionizing the security landscape in the financial technology (FinTech) sector, enhancing safety measures and fostering trust among users. This review explores the role of AI-driven biometrics in securing FinTech operations, highlighting its benefits and implications. AI-driven biometrics refers to the use of artificial intelligence (AI) algorithms to analyze biological data for authentication purposes. This technology has gained significant traction in the FinTech sector due to its ability to provide a higher level of security than traditional authentication methods. By analyzing unique biological traits such as fingerprints, facial features, and voice patterns, AI-driven biometrics can accurately verify the identity of users, making it difficult for unauthorized individuals to gain access to sensitive financial information. One of the key benefits of AI-driven biometrics in FinTech is its ability to enhance security measures. Traditional authentication methods such as passwords and PINs are increasingly vulnerable to hacking and fraud. AI-driven biometrics, on the other hand, offers a higher level of security by using biological traits that are unique to each individual. This makes it significantly more difficult for fraudsters to gain access to sensitive financial information. In addition to enhancing security, AI-driven biometrics also improves the user experience. By replacing traditional authentication methods with biometric authentication, users can access their accounts more quickly and conveniently, without the need to remember complex passwords or PINs. This not only improves the overall user experience but also reduces the risk of user error and account lockouts. Overall, AI-driven biometrics is pioneering safety and trust in the FinTech sector by providing a higher level of security and enhancing the user experience. As this technology continues to evolve, it is likely to play an increasingly important role in securing financial transactions and fostering trust among users.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

Wearable sensors for monitoring vital signals in sports and health: progress and perspective

Purpose This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics. Design/methodology/approach In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices. Findings This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution. Originality/value The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

AI-Powered Revolution: Transforming Industrial Production through Automation

The integration of artificial intelligence (AI) tools has propelled automation in industrial production to new heights, ushering in a wave of transformative advancements. This research delves into the profound impact of AI tools on industrial production, elucidating their pivotal role in driving efficiency, cost reduction, and productivity enhancements. By leveraging AI algorithms and machine learning techniques, manufacturers can optimize operations with unprecedented precision and agility. However, alongside the promises of increased efficiency come significant challenges and opportunities. This paper navigates through the complexities of implementing AI-driven automation in manufacturing processes, addressing issues such as data security, workforce adaptation, and ethical considerations. Through rigorous secondary data analysis and research methodology, this study endeavours to shed light on the current state of automation in industrial production while offering valuable insights into its future prospects and implications for the manufacturing industry.

A real-time feeding decision method based on density estimation of farmed fish

With the global population growth and increasing demand for high-quality protein, aquaculture has experienced rapid development. Fish culture management and feed supply are crucial components of aquaculture. Traditional baiting management relies on experiential judgment and regular observation, which often leads to inefficient baiting practices and wastage. To address these issues, intelligent bait casting decisions have emerged. Leveraging advanced artificial intelligence algorithms, intelligent bait casting decisions can overcome most drawbacks of traditional bait management and enhance breeding efficiency. However, most of the current intelligent baiting decisions are focused on using methods such as image processing and target detection to identify different feeding actions and patterns. These methods do not discuss based on video streams and do not consider the changes in fish behavior during the baiting process. Therefore, we proposed a real-time analysis method based on the density distribution of fish feeding behavior (FishFeed). Firstly, this method upgrades the input mechanism, not only handling static images but also capable of real-time video stream analysis. Secondly, by evaluating the fish school density distribution through a new intelligent baiting strategy, this method can monitor the feeding behavior of fish school during the baiting process in real time. Finally, we constructed a dataset for fish school density analysis (DlouFishDensity) that includes a wealth of video and image frames, providing a valuable resource for research. Experimental results indicate that our algorithm outperforms MCNN, improving MAE by 1.63 and 1.35, MSE by 1.92 and 1.58, and reducing prediction time by 2.56 seconds on the same dataset. By implementing real-time analysis of fish feeding behavior density distribution, our method offers a more efficient and effective approach to baiting management in aquaculture, contributing to improved breeding efficiency and resource utilization.

Impact of Fintech on Investment Banking

Fintech, which stands for financial technology, has emerged as a disruptive force in various industries, and its impact on investment banking has been particularly profound. The rapid advancement of technology, coupled with the growing demand for more efficient and accessible financial services, has paved the way for innovative fintech applications within the investment banking sector. Fintech platforms offer advanced data analytics, artificial intelligence (AI), and machine learning tools that enable investors to process vast amounts of information and make data-driven investment decisions. This has not only increased the speed and accuracy of investment research but has also levelled the playing field, allowing smaller investors and even individual retail investors to access high-quality research previously reserved for institutional players. Another area where fintech applications are transforming investment banking is in the realm of online trading. Fintech platforms provide seamless and user-friendly interfaces that allow investors to execute trades on a real-time basis. The integration of AI algorithms and robo-advisory services within these platforms has expanded the reach of automated investment advice, enabling investors to receive personalized recommendations based on their investment goals, risk appetite, and financial constraints. fintech platforms have introduced alternative methods of capital formation, such as crowdfunding and peer-to-peer lending. This has not only reduced costs but has also increased access to capital for small and medium-sized enterprises (SMEs) and startups, fostering innovation and entrepreneurial activity. As technology continues to advance and regulatory frameworks adapt, investment banking is likely to witness even more profound changes, driving innovation and shaping the future of the industry.

Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm

Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm

An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study.

Alcohol-associated hepatitis (AH) poses significant short-term mortality. Existing prognostic models lack precision for 90-day mortality. Utilizing artificial intelligence (AI) in a global cohort, we sought to derive and validate an enhanced prognostic model. The Global AlcHep initiative, a retrospective study across 23 centers in 12 countries, enrolled AH patients per NIAAA criteria. Centers were partitioned into derivation (11 centers, 860 patients) and validation cohorts (12 centers, 859 patients). Focusing on 30 and 90-day post-admission mortality, three AI algorithms (Random Forest, Gradient Boosting Machines, and eXtreme Gradient Boosting) informed an ensemble model, subsequently refined via Bayesian updating, integrating the derivation cohort's average 90-day mortality with each center's approximate mortality rate to produce post-test probabilities. The ALCoholic Hepatitis Artificial INtelligence (ALCHAIN) Ensemble score integrated age, gender, cirrhosis, and 9 laboratory values, with center-specific mortality rates. Mortality was 18.7% (30-day) and 27.9% (90-day) in the derivation cohort, versus 21.7% and 32.5% in the validation cohort. Validation cohort 30 and 90-day AUCs were 0.811 (0.779 - 0.844) and 0.799 (0.769 - 0.830), significantly surpassing legacy models like Maddrey's Discriminant Function, MELD variations, ABIC, Glasgow, and modified Glasgow Scores (p<0.001). ALCHAIN Ensemble score also showcased superior calibration against MELD and its variants. Steroid use improved 30-day survival for those with an ALCHAIN Ensemble score>0.20 in both derivation and validation cohorts. Harnessing AI within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications, it's accessible at: https://aihepatology.shinyapps.io/ALCHAIN/.

Application of Closed-loop Theory in Deep Learning Training Guided by High-strength Intelligent Machinery

Artificial intelligence (AI) algorithms and continuous monitoring technologies have the potential to transform the way chronic illnesses are managed. We will also talk about the problems and potential that AI technology presents for CGM in individualised and preventive medicine. Furthermore, we assessed the AHCL system's usefulness in patients with impaired awareness of hypoglycemia (IAH) and those who correctly recognised hypoglycemia symptoms. The participants' ages varied from 37 to 15, and they had received diabetes medication for an average of 20 to 10 years. IAH was seen in 12 individuals (27%) with a Clarke's score of less than 3. Patients with IAH were older than those who did not have IAH. The baseline CGM readings and A1c were the same, but the estimated glomerular filtration rate (eGFR) was lower. Despite prior insulin treatment, the AHCL system resulted in an overall drop in A1c (from 6.9 0.5% to 6.7 0.6%, P 0.001). Only three patients (7%) received Clarke's three scores after six months on the AHCL system, resulting in a 20% absolute risk decrease for IAH (95% confidence interval: 7-32).  

Now you see me, now you don't: an exploration of religious exnomination in DALL-E.

Artificial intelligence (AI) systems are increasingly being used not only to classify and analyze but also to generate images and text. As recent work on the content produced by text and image Generative AIs has shown (e.g., Cheong et al., 2024, Acerbi & Stubbersfield, 2023), there is a risk that harms of representation and bias, already documented in prior AI and natural language processing (NLP) algorithms may also be present in generative models. These harms relate to protected categories such as gender, race, age, and religion. There are several kinds of harms of representation to consider in this context, including stereotyping, lack of recognition, denigration, under-representation, and many others (Crawford inSoundings 41:45-55,2009; in: Barocas et al., SIGCIS Conference, 2017). Whereas the bulk of researchers' attention thus far has been given to stereotyping and denigration, in this study we examine 'exnomination', as conceived by Roland Barthes (1972), of religious groups. Our case study is DALL-E, a tool that generates images from natural language prompts. Using DALL-E mini, we generate images from generic prompts such as "religious person." We then examine whether the generated images are recognizably members of a nominated group. Thus, we assess whether the generated images normalize some religions while neglecting others. We hypothesize that Christianity will be recognizably represented more frequently than other religious groups. Our results partially support this hypothesis but introduce further complexities, which we then explore.

The Use of Lidar and Artificial Intelligence Algorithms for Detection and Size Estimation of Potholes

Road potholes have a well-known impact on driving quality and safety. Therefore, timely mitigation of potholes is critical for the safety of road users. However, efficient and timely maintenance relies on the presence of an effective process for pothole detection. Currently, transportation agencies primarily rely on manual inspection and road user reporting. These methods are subjective, prone to inaccuracy, and some are also laborious and time-consuming. An ideal pothole detection system would be accurate, objective, automated, and relatively inexpensive. In this context, accuracy encompasses three distinct performance areas: detection, localization, and size estimation. This study explores the potential of utilizing a mobile light detection and ranging (LiDAR) for accurate detection and size estimation, along with a global navigation satellite system (GNSS) receiver for localization, to develop an effective pothole surveillance system. To achieve this objective, the study proposes a four-step framework. Firstly, the LiDAR data are processed to generate ring-wise cross-sectional images. Secondly, a deep learning object detection network is trained to predict the presence and size of potholes. Thirdly, the ring-wise inferences are aggregated to produce a final decision. Lastly, the aggregated inferences are synchronized with GNSS locations to generate inspection maps. The system’s performance was validated using multiple road strips, never seen by the model, containing potholes of different sizes and shapes. The results demonstrated the effectiveness and accuracy of the proposed system. Overall, this research contributes to the research on LiDAR-based pothole inspection by proposing a novel four-step framework and incorporating it into an end-to-end pothole detection system, which can greatly improve the efficiency of pothole maintenance and enhance the safety of road users.

Dynamic identification methods and artificial intelligence algorithms for damage detection of masonry infills

AbstractThe failure of non-structural components after an earthquake is among the most expensive earthquake-incurred damage, and may also have life-threatening consequences, especially in public buildings with very crowded facilities, because exposition is high and the risk increases accordingly. The assessment of existing non-structural components is particularly complex because in-depth in situ investigation is necessary to detect the presence of deficiencies or damage. This problem concerns interior and exterior partitions made of various materials (e.g., glass and masonry), as well as equipment and facilities in construction (building, industry, and infrastructure). Defining the boundary conditions of these components is of paramount importance. Indeed, external restraints (i) affect dynamic properties and, thus, the action experienced during an earthquake, and (ii) influence the capacity to detach the component before failure from the bearing structure (e.g., an infill wall connected to the main structural frame, or equipment connected to secondary structural members such as floors). The authors, therefore, conducted environmental vibration tests of an infill wall and refined a finite element model to simulate typical damage scenarios to be implemented on the wall. Selected damage scenarios were then artificially realized on the existing infill and further ambient vibration tests were performed to measure the accelerations for each of them. Finally, the authors used these accelerations to detect the damage by means of established OMA, as well as innovative machine learning techniques. The results showed that convolutional variational autoencoders (CVAE), coupled with a one-class support vector machine (OC-SVM), identified the anomaly even when the OMA exhibited limited effectiveness. Moreover, the machine learning procedure minimizes human interaction during the damage detection process.

The use of artificial intelligence in the diagnosis and detection of complications of diabetes

Introduction: Diabetes poses a significant global health challenge, impacting patient well-being and longevity. Despite advances in diagnosis and treatment, the prevalence of diabetes continues to rise, with projections indicating a substantial increase in affected individuals in the coming years. The complications of diabetes, including cardiovascular disease, retinopathy, nephropathy, and neuropathy, underscore the importance of early detection and management. In this context, artificial intelligence (AI) offers promising opportunities to revolutionize diabetes care, enabling faster diagnostics, more effective treatment strategies.&#x0D; Description of the State of Knowledge: Artificial intelligence (AI) has emerged as a transformative force in healthcare, leveraging machine learning and deep learning algorithms to analyze vast amounts of medical data. These algorithms enable more accurate diagnosis, prediction of disease onset, and early detection of complications associated with diabetes. Machine learning models, including support vector machines and neural networks, have shown promise in identifying diabetes risk factors and predicting disease progression. Deep learning techniques, with their ability to analyze complex data patterns, offer further insights into diabetes diagnosis. Additionally, fuzzy cognitive maps provide a framework for decision-making based on patient data, enhancing early detection efforts.&#x0D; Summary: Artificial intelligence holds immense potential to transform diabetes care, offering solutions for early detection, personalized treatment, and improved patient outcomes. By harnessing the power of AI algorithms, healthcare providers can enhance diagnostic accuracy, predict disease progression, and implement targeted interventions.

Optimization and Performance Evaluation of Artificial Intelligence Algorithms in Game Design

This article aims to optimize artificial intelligence algorithms in game design to enhance game performance and player experience. Therefore, an innovative method called Dynamic Game AI Fitness Optimization Algorithm (DGAFOA) was proposed in the article, and its effectiveness was verified through experiments. In the experimental section, this article applies the DGAFOA algorithm to a typical role-playing game and compares it with traditional fixed parameter AI algorithms. The experimental results show that the DGAFOA algorithm exhibits significant advantages in key indicators such as game task completion rate and player satisfaction. Specifically, game AI using the DGAFOA algorithm can respond more quickly and accurately to player behavior, improving the overall smoothness and fun of the game. In addition, the DGAFOA algorithm also introduces ε- The greedy strategy balances exploration and utilization, effectively avoiding the problem of AI getting stuck in local optima. This enables game AI to maintain stable performance while still possessing the ability to explore new strategies, bringing players more surprises and challenges.

REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION

Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction of energy demand. One of the key advantages of AI in energy efficiency optimization is its ability to adapt and learn from data, leading to continuous improvement in energy-saving strategies. AI algorithms can optimize energy consumption based on factors such as weather conditions, occupancy patterns, and energy prices, resulting in significant cost savings and environmental benefits. Furthermore, AI enables the integration of renewable energy sources into existing energy systems by predicting renewable energy generation and optimizing its use. This integration helps reduce reliance on fossil fuels and mitigates greenhouse gas emissions, contributing to a more sustainable energy future. However, the implementation of AI in energy efficiency optimization is not without challenges. These include data privacy concerns, the need for specialized skills to develop and deploy AI solutions, and the complexity of integrating AI systems into existing energy infrastructure. Addressing these challenges will be crucial for realizing the full potential of AI in energy efficiency optimization. In conclusion, AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems. By leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future.&#x0D; Keywords: Role, AI, Energy, Efficiency, Optimization.

Remote Sensing and AI-based Monitoring of Legume Crop Health and Growth

Background: For the enhancement of agricultural productivity, while ensuring sustainability, this study delves into the under-explored domain of monitoring legume crop health and growth. Traditional methods of crop assessment encounter limitations, prompting a push for innovation by integrating advanced remote sensing technologies and artificial intelligence (AI). The purpose is to revolutionize crop assessment techniques and overcome existing constraints. Methods: The data was collected using a combination of satellite imagery and ground-based sensors, resulting in a rich repository of multispectral and spatial information. By using the capabilities of AI, a robust model was developed to interpret the gathered data, offering a detailed insight into the health and growth dynamics of legume crops. The AI algorithms not only identify anomalies but also forecast future states, facilitating timely interventions and informed decision-making in agriculture. Result: The findings of this study signify a significant change in precision agriculture, where the synergy of remote sensing and AI optimizes resource allocation, minimizes environmental impact and maximizes crop yields. The research unlocks the potential to transform legume farming practices, promoting sustainability and ushering in an era of data-driven cultivation. The implications extend beyond the legume crop sector, influencing the broader agricultural landscape with the promise of more efficient and sustainable practices.

THE IMPACT OF SMART GRIDS ON ENERGY EFFICIENCY: A COMPREHENSIVE REVIEW

Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector.&#x0D; Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on type and intensity are integral components of DM treatment. The continuous glucose monitoring system (CGMS) signal provides the blood glucose (BG) level, and the combination of CGMS and heart rate (HR) signals are potential targets for detecting relevant physical activity from the BG variation point of view. The main objective of the present research is the developing of an artificial intelligence (AI) algorithm capable of detecting physical activity using these signals. Using multiple recurrent models, the best-achieved performance of the different classifiers is a 0.99 area under the receiver operating characteristic curve. The application of recurrent neural networks (RNNs) is shown to be a powerful and efficient solution for accurate detection and analysis of physical activity in patients with DM. This approach has great potential to improve our understanding of individual activity patterns, thus contributing to a more personalized and effective management of DM.

The Digital Transformation of College English Classroom: Application of Artificial Intelligence and Data Science

A major step forward in educational technology is the application of Data Science additionally Artificial Intelligence (AI) into undergraduate English courses. Improving teaching approaches and student involvement in the context of English language acquisition is an important issue that this study seeks to address. Even though there have been great strides in educational technology, conventional English classes still have a hard time meeting the demands of their different student bodies and offering individualized lessons. This is a major problem that prevents English language training from being effective, according to the material that is already available. In this study, we provide an approach to this issue called English Smart Classroom Teaching with the Internet of Things (ESCT-IoT). Utilizing data science techniques, artificial intelligence (AI) algorithms, and Internet of Things (IoT) sensors, ESCT-IoT intends to provide a personalized learning environment that is both immersive and adaptable. The fuzzy hierarchical evaluation technique is used to determine the assessment's final result, which measures the smart classroom's instructional impact. To overcome the limitations of conventional education, ESCT-IoT gathers and analyses data in real time to give adaptive material, individualized feedback, and learning suggestions. There are noticeable benefits as compared to traditional methods of instruction when it comes to evaluation metrics like student engagement, learning outcomes, and teacher satisfaction. Furthermore, ESCT-IoT is excellent in encouraging active learning, improving language fluency, and boosting overall academic achievement, according to qualitative comments from both students and teachers.