Big Datasets Research Articles

Deep Learning Approaches for Hate Speech Detection

The main goals of this project are to develop reliable hate speech identification models that can recognize derogatory terminology, prejudiced attitudes, and damaging stereotypes on a variety of internet platforms. To help the models train and generalize efficiently, the study focuses on using big datasets with a variety of content types, including both hate speech and non-hate speech. The findings of this study suggest that machine learning has the potential to mitigate the negative consequences of hate speech by using automated filtering and flagging tools. The study also highlights the need for continued research and development to improve the accuracy, uniformity, and transparency of hate speech detection systems, and ultimately to foster a safer online environment to encourage all people.

Probing the limit of hydrologic predictability with the Transformer network

For a number of years since their introduction to hydrology, recurrent neural networks like long short-term memory (LSTM) networks have proven remarkably difficult to surpass in terms of daily hydrograph metrics on community-shared benchmarks. Outside of hydrology, Transformers have now become the model of choice for sequential prediction tasks, making it a curious architecture to investigate for application to hydrology. Here, we first show that a vanilla (basic) Transformer architecture is not competitive against LSTM on the widely benchmarked CAMELS streamflow dataset, and lagged especially prominently for the high-flow metrics, perhaps due to the lack of memory mechanisms. However, a recurrence-free variant of the Transformer model obtained mixed comparisons with LSTM, producing very slightly higher Kling-Gupta efficiency coefficients (KGE), along with other metrics. The lack of advantages for the vanilla Transformer network is linked to the nature of hydrologic processes. Additionally, similar to LSTM, the Transformer can also merge multiple meteorological forcing datasets to improve model performance. Therefore, the modified Transformer represents a rare competitive architecture to LSTM in rigorous benchmarks. Valuable lessons were learned: (1) the basic Transformer architecture is not suitable for hydrologic modeling; (2) the recurrence-free modification is beneficial, so future work should continue to test such modifications; and (3) the performance of state-of-the-art models may be close to the prediction limits of the dataset. As a non-recurrent model, the Transformer may bear scale advantages for learning from bigger datasets and storing knowledge. This work lays the groundwork for future explorations into pretraining models, serving as a foundational benchmark that underscores the potential benefits in hydrology.

Texture prediction of natural soils in the Brazilian Amazon through proximal sensors

Proximal sensors provide fast, low-cost, environmentally friendly, and reliable analyses for the characterization of soils and other materials. Numerous studies have been conducted on soils in temperate regions, but there are knowledge gaps regarding the use of these devices in tropical soils, especially in the Amazon region. In this regard, this study utilized portable proximal sensors of X-ray fluorescence spectroscopy (pXRF) and diffuse reflectance spectroscopy in the visible to near-infrared region (Vis-NIR) for predicting the texture of natural soils in 61 municipalities in the state of Pará, Amazon region, Brazil. The objectives were: i) to investigate the accuracy of soil texture prediction based on data from sensors separately and sensor fusion (pXRF and Vis-NIR data) using two supervised algorithms (Random Forest, RF, and Support Vector Machine, SVM) and ii) to assess the effect of soil horizon (superficial and subsuperficial horizons, and their combination) in predicting the texture of tropical natural soils. In total, 233 soil samples were collected in the 0–20 cm and 80–100 cm depths, equivalent to superficial and subsuperficial horizons in areas with primary or secondary forest cover with at least 20 years of natural regeneration and approximately 20 ha of coverage area. The hydrometer method was used for soil texture analysis. In parallel, a portion of the soil samples was analyzed by pXRF and Vis-NIR, in triplicate, under laboratory conditions. The predictive models with RF were more robust compared to the models obtained with SVM, according to ratio performance interquartile distance (RPIQ), coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The R2 values obtained by pXRF, Vis-NIR, and sensor data combination were, respectively, 0.89, 0.87, and 0.93 for sand; 0.92, 0.90, and 0.93 for clay; and 0.91, 0.67, and 0.93 for silt. Overall, clay prediction models achieved higher R2 values compared to sand and silt models. Soil texture prediction using sensor fusion showed lower RMSE values and higher R2 and RPIQ values, respectively (sand: 7.79, 0.93, 4.69; clay: 5.58, 0.93, 3.86; and silt: 5.72, 0.92, 2.92) compared to the best-performing sensor individually (Vis-NIR). With regard to the optimal model utilizing individual sensor data, Vis-NIR models exhibited reduced error for clay and sand prediction. The effect of combining horizons to a single and bigger dataset was minimally important for the models. The results demonstrate confidence in the use of proximal sensors for soil texture assessment in natural Amazon soils, aiming to reduce costs and the time required for analyses.

A calibrated data‐driven approach for small area estimation using big data

SummaryWhere the response variable in a big dataset is consistent with the variable of interest for small area estimation, the big data by itself can provide the estimates for small areas. These estimates are often subject to the coverage and measurement error bias inherited from the big data. However, if a probability survey of the same variable of interest is available, the survey data can be used as a training dataset to develop an algorithm to impute for the data missed by the big data and adjust for measurement errors. In this paper, we outline a methodology for such imputations based on an k‐nearest neighbours (kNN) algorithm calibrated to an asymptotically design‐unbiased estimate of the national total, and illustrate the use of a training dataset to estimate the imputation bias and the “fixed‐k asymptotic” bootstrap to estimate the variance of the small area hybrid estimator. We illustrate the methodology of this paper using a public‐use dataset and use it to compare the accuracy and precision of our hybrid estimator with the Fay–Harriot (FH) estimator. Finally, we also examine numerically the accuracy and precision of the FH estimator when the auxiliary variables used in the linking models are subject to undercoverage errors.

A Comprehensive Exploration of Text, Web, Social Media, and Geospatial Analytics for Informed Decision Making

Text Analytics is the process of turning unstructured text data into useful information for analysis, to gauge consumer opinions, feedback, and product evaluations. It also provides search functionality, entity modelling, and emotional analysis to enable fact-based decision making. Analysis of website visitor behaviour is done using Web Analytics. The number of websites and users on the internet is growing daily. It is the process of tracking website information that can help to improve the web application features and evaluate the behaviour of users. By performing social media analytics on social media for example twitter, Instagram, Facebook. Data such as likes, comments, shares and saves can be obtained and analyse to know how the society think about the product. Geospatial analysis is used to make visualizations that include maps, graphs, statistics that show data according to geographic location. This is important to be analysed as it tells which area or country has the highest product sold or lowest subscription of the service. To make the data much easier for human brain to analyses or to make a conclusion, data visualization design is the process of putting all data information collected into a visual context. For instance, graph or map. The main objectives of data visualization are to make it simpler or easier to spot the outliers and patterns trends in big data sets. There are a variety of clustering processes or techniques available to arrange the data efficiently to its related data. The clustering process that is used in data mining is presented in this work.

Developing a novel big dataset and a deep neural network to predict the bearing capacity of a ring footing

The accurate prediction of the bearing capacity of ring footings, which is crucial for civil engineering projects, has historically posed significant challenges. Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets. To overcome these limitations, a comprehensive finite element limit analysis (FELA) was conducted to predict the bearing capacity of ring footings. The study considered a range of effective parameters, including clay undrained shear strength, heterogeneity factor of clay, soil friction angle of the sand layer, radius ratio of the ring footing, sand layer thickness, and the interface between the ring footing and the soil. An extensive dataset comprising 80,000 samples was assembled, exceeding the limitations of previous research. The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity. In light of the time-intensive nature of gathering a substantial dataset, a customized deep neural network (DNN) was developed specifically to predict the bearing capacity of the dataset rapidly. Both computational and comparative results indicate that the proposed DNN (i.e. DNN-4) can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error (MSE) below 0.009 in a fraction of 1 s, reflecting the effectiveness and efficiency of the proposed method.

Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Point-of-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

More detailed and precise mobility patterns are needed for policies to reduce monomodal automotive dependency and promote multimodality in travel behaviors. Yet, empirical evidence from an integrated view of a complete door-to-door trip mode chain with daily mobility for pattern identification is still lacking. As an improvement and a solution on this issue, a multi-layer cluster model was designed and proposed for distinguishing 20 mobility pattern clusters, including six monomodal traveler groups, two non-transit multimodal traveler groups, and 12 transit multimodal based on big data mining. Statistical analysis with seven indicator measurements and a spatial distribution analysis with the Kernel density GIS maps of travelers’ residential location were carried out to reveal significant disparities across pattern clusters concerning spatial, social, and trip characteristics, based on which more precise and target policies for each group were discussed. This research may help provide more detailed information in establishing traveler mobility pattern profiles and solutions in filling the planning–implementation gap from the perspective of planners, policymakers, and travelers.

HealthTech Companion: Promoting Access to Personalized Medical Advice

Abstract: Science has come a long way in the last several years, thanks in large part to machine learning tools. This improvement is most noticeable in the field of medical diagnostics, where it is now much easier to diagnose disorders based only on symptoms. Although there are frequently similarities between the symptoms, each disease presents with its own distinct set of signs. It is therefore essential for a diagnosis to be made correctly to identify patterns within these symptoms. However, the sheer number of illnesses and the symptoms that go along with them provide a formidable obstacle to anybody trying to identify their own health issues. Picture yourself feeling sick, but not knowing why you are feeling so bad. This is where a useful chatbot may be quite beneficial, letting users enter their symptoms and acting as an informed guide in making educated guesses about possible illnesses. The Apriori algorithm, which is well-known for its capacity to find patterns in big datasets by linking objects together, is one potent tool used in this situation. Within the healthcare industry, the Apriori algorithm is particularly good at identifying patterns of disease by associating it with related symptoms. This allows the chatbot to provide well-informed information in response to user inputs. Apart from the Apriori method, Recurrent Neural Networks (RNNs) are also utilized due to their ability to handle sequential input and produce replies to user queries that are relevant for the context. The chatbot gives people the ability to evaluate health issues even in the lack of expert medical knowledge by integrating these algorithms. This gives people the ability to take charge of their health by quickly requesting help and according to the chatbot's instructions. Furthermore, the use of Quantum Machine Learning methods enhances the chatbot's skills even further. A viable path to improving illness prediction accuracy and honing the chatbot's recommendations is through the use of quantum algorithms, which can handle complicated data structures and carry out calculations that are beyond the capabilities of conventional algorithms Because these sophisticated algorithms enable early diagnosis, diseases may be treated and lifestyle changes can be made earlier, improving prognoses and boosting survival rates. Additionally, early identification shortens the time it takes for illnesses to worsen

Multi-variants vision transformer-based malware image classification model using multi-criteria decision-making

Visualization-based malware detection gets more and more attention for detecting sophisticated malware that traditional antivirus software may miss. The approach involves creating a visual representation of the memory or portable executable files (PEs). However, most current visualization-based malware classification models focus on convolution neural networks instead of Vision transformers (ViT) even though ViT has a higher performance and captures the spatial representation of malware. Therefore, more research should be performed on malware classification using vision transformers. This paper proposes a multi-variants vision transformer-based malware image classification model using multi-criteria decision-making. The proposed method employs Multi-variants transformer encoders to show different visual representation embeddings sets of one malware image. The proposed architecture contains five steps: (1) patch extraction and embeddings, (2) positional encoding, (3) multi-variants transformer encoders, (4) classification, and (5) decision-making. The variants of transformer encoders are transfer learning-based models i.e., it was originally trained on ImageNet dataset. Moreover, the proposed malware classifier employs MEREC-VIKOR, a hybrid standard evaluation approach, which combines multi-inconsistent performance metrics. The performance of the transformer encoder variants is assessed both on individual malware families and across the entire set of malware families within two datasets i.e., MalImg and Microsoft BIG datasets achieving overall accuracy 97.64 and 98.92 respectively. Although the proposed method achieves high performance, the metrics exhibit inconsistency across some malware families. The results of standard evaluation metrics i.e., Q, R, and U show that TE3 outperform the TE1, TE2, and TE4 variants achieving minimal values equal to 0. Finally, the proposed architecture demonstrates a comparable performance to the state-of-the-art that use CNNs.

Enhancing Corporate Performance Through Transformational Leadership in AI-driven ERP Systems

Organisational traits, technological adoption, and transformative leadership in Chinese enterprises are examined in this study using a dataset 2010–2022. The main goal is to understand how transformative leadership affects business performance and how AI-driven ERP systems, organisational scale, and technology adoption culture modulate this relationship. The paper provides a solid framework for understanding these complex linkages using a big dataset using R and SPSS statistical analysis. Revolutionary leadership consistently boosts business. Organisational size mediates and revolutionary leadership may work better in larger organisations. Technology adoption culture highlights an organization's readiness to accept new technology, regulating the relationship. The study also found that AI-driven ERP systems diminish the correlation, suggesting that higher-tech organisations benefit from transformational leadership. This information can help CEOs adjust their technology and company characteristics strategies. Leadership theories benefit from theoretical implications that highlight contextual aspects that affect leadership dynamics. Understanding how organisational culture affects AI-driven revolutionary leadership may also help. Integrating AI and ethical transformative leadership affects sustainability and trust. Finally, the innovative leadership of AI-driven ERP systems in numerous areas and sizes explains it.

Detection of Fake Online Reviews Using Semi Supervised and Supervised Learning

Consumers reviews on ecommerce websites, online services, ratings and experience stories are useful for the user as well as the vendor. The reviewer can increase their brand’s loyalty and help other customers understand their experience with the product. Similarly reviews help the vendors gain more profiles by increasing their sale of products, if consumers leave positive feedback on their product review. But unfortunately, these review mechanisms can be misused by vendors. For example, one may create fake positive reviews to promote brand’s reputation or try to demote competitor’s products by leaving fake negative reviews on their product. Existing solutions with supervised include application of different machine learning algorithms and different tools like Weka. Unlike the existing work, instead of using a constrained dataset I chose to have a wide variety of vocabulary to work on such as different subjects of datasets combined as one big data set. Sentiment analysis has been incorporated based on emojis and text content in the reviews. Fake reviews are detected and categorized. The testing results are obtained through the application of Naïve Bayes, Linear SVC, Support Vector Machine and Random forest algorithms. The implemented (proposed) solution is to classify these reviews into fake or genuine. The highest accuracy is obtained by using Naïve Bayes by including sentiment classifier.

EEG-based brain-computer interface methods with the aim of rehabilitating advanced stage ALS patients

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms.

AI-Based Facial Emotion Recognition

Facial expression, age, and gender detection have received a lot of interest in recent years due to their wide range of applications in fields including healthcare, security, marketing, and entertainment. With the proliferation of artificial intelligence (AI) techniques, especially deep learning, facial analysis systems' accuracy and efficiency have improved significantly. This paper provides a comprehensive analysis of the most recent advances in AI-based facial expression, age, and gender identification algorithms. We explore the constraints and limitations of current techniques, such as the necessity for big annotated datasets, biases in training data, and interpretability issues in deep learning models. This study seeks to give academics, practitioners, and policymakers, a thorough grasp of the most recent state-of-the-art methodologies and trends in AI-based facial emotion, age, and gender identification, thereby supporting future advancements and responsible usage of these technologies. Keywords: Facial expression, age, gender, identification, deep learning, artificial intelligence

Malware Detection Using Machine Learning

In this work, we present a flexible system that makes use of various machine learning methods to efficiently distinguish between malware and clean files while purposefully reducing false positives. In the field of cybersecurity, our strong framework is both flexible and strong, working along with different machine learning algorithms. Our study unfolds with an exploration of basic principles using the Random Model, K Nearest Neighbouring Classifier (KNN), and Logistic Regression as foundational parts, emphasizing the differentiation between malware and benign files. Extensive experiments on mediumsized datasets that include malware and clean files verify the effectiveness of our methodology. The system then goes through a painstaking scaling-up process that guarantees smooth operation with big datasets containing both malware and clean files. Our methodology is validated by analysing three important algorithms: Random Model, KNN, and Logistic Regression, each of which adds unique advantages to the malware detection system. The evaluation, which is carried out on several datasets, aims to minimize false positives while striking a compromise between precision and recall. Finally, our flexible system, implemented and evaluated on many datasets, demonstrates its efficacy in distinguishing malware from clean files. The framework's flexibility and scalability make it an invaluable tool in the everevolving field of cybersecurity, providing a sophisticated method of malware detection. The proposed algorithms emphasize the framework's potential as a supplementary tool to current cybersecurity measures while also adding to its reliability. Keywords— ML, KNN, RM, LR

Harnessing the Power of Emerging Digital Technologies for improved Sustainability and Productivity in Biomedical Engineering and Neuroscience

This article discusses the potential of using digital technologies like blockchain, AI, IoT and big data analytics to improve resource efficiency, supply chain management, decision making and reduce fraud. It highlights how blockchain can create a transparent and tamper-proof system for tracking forest products from harvest to sale, reducing fraud and improving sustainability. The implementation of blockchain is argued to lead to substantial improvements in supply chain management through increased transparency and traceability. Moreover, AI, blockchain, IoT and advanced analytics are said to help optimize production and supply chains, detect fraud early, and improve forecasting and risk management for more efficient policies. The adoption of technologies like IoT, smart sensors and standardized data protocols is also outlined as a way to revolutionize sectors like agriculture, manufacturing and energy through enhanced productivity and efficiency. Emerging digital technologies like artificial intelligence, bioengineering, internet of things, data analytics and digital transformation offer immense potential to address global challenges and drive progress. Artificial intelligence and machine learning algorithms can be applied to improve healthcare, education, transportation and resource management by processing large datasets. When combined with IoT, these technologies enable precision agriculture, predictive maintenance, and smart infrastructure for sustainable development. Advancements in bioengineering such as synthetic biology, biomimicry and biomanufacturing aim to solve problems in medicine, energy and environment by emulating nature. IoT connects physical objects through sensors and networks to collect and exchange data, allowing devices, vehicles and systems to become intelligent and autonomous. Analytics on these Big Data sets from connected products, facilities and natural systems help reveal valuable insights for evidence-based decision making. Digital transformation of industries and business models leverages these technologies to enhance productivity, efficiency and consumer experiences through virtual and augmented realities, cloud computing and automated processes. However, responsible innovation through interdisciplinary research and governance is required to ensure such progress addresses societal needs, reduces inequalities and minimizes environmental impacts. Cross-sector collaborations involving industry, academia and policymakers can help maximize benefits while preemptively mitigating potential risks.

A Novel and Efficient Spatio-Temporal Co-location Pattern Mining Algorithm

Colocation pattern mining approaches aim at discovering neighboring relationships of distinct spatial features in geographic and temporal space.With big spatio-temporal dataset, there is large number of patterns often discovered. Then it is of importance to discover meaningful and patterns of interest which come as an aid in applications in use for humans and commercial use. To discover interesting patterns, we present in this article a co-location pattern mining algorithmic framework by considering neighbourhood, clustering, hashing, and mining methods. Neighbourhood relationship describes the closeness between the entities. The results of neighbourhood could be varied by varying the distance threshold. These objects exhibiting neighbouring entities are grouped using clustering technique. Clustering is a classical research approach that produces grouping of the entities. A clustering technique has been presented in the paper for spatio-temporal dimension that offers the advantage of faster grouping based on the neighbourhood relationship. Finally, a hash structure is utilized for faster access and retrieval of patterns. The proposed mining algorithm along with the distance and time threshold efficiently discovers the interesting spatio-temporal patterns and validates the patterns. The experiment conducted shows that the proposed co-location algorithm method yields effective and efficient outcome.

Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research. The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms. A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics. The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent. We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively.

An eXplainable Artificial Intelligence Methodology on Big Data Architecture

Although artificial intelligence has become part of everyone’s real life, a trust crisis against such systems is occurring, thus increasing the need to explain black-box predictions, especially in the military, medical, and financial domains. Modern eXplainable Artificial Intelligence (XAI) techniques focus on benchmark datasets, but the cognitive applicability of such solutions under big data settings is still unclear due to memory or computation constraints. In this paper, we extend a model-agnostic XAI methodology, named Cluster-Aided Space Transformation for Local Explanation (CASTLE), to be able to deal with high-volume datasets. CASTLE aims to explain the black-box behavior of predictive models by combining both local (i.e., based on the input sample) and global (i.e., based on the whole scope for action of the model) information. In particular, the local explanation provides a rule-based explanation for the prediction of a target instance as well as the directions to update the likelihood of the predicted class. Our extension leverages modern big data technologies (e.g., Apache Spark) to handle the high volume, variety, and velocity of huge datasets. We have evaluated the framework on five datasets, in terms of temporal efficiency, explanation quality, and model significance. Our results indicate that the proposed approach retains the high-quality explanations associated with CASTLE while efficiently handling large datasets. Importantly, it exhibits a sub-linear, rather than exponential, dependence on dataset size, making it a scalable solution for massive datasets or in any big data scenario.

A Hybrid IoT-Enabled Transfer Learning Model for Accurate Diagnosis of Coronary Heart Disease

Coronary heart disease (CHD) is the top cause of death in the world, which shows how important it is to get a correct evaluation as soon as possible. The Internet of Things (IoT) opens up new ways to improve healthcare, and transfer learning has shown promise in making machine learning models work better, especially when it comes to medical analysis. IoT-Enabled Transfer Learning Model (HIETLM), which is new, is proposed in this study as a way to accurately diagnose congenital heart disease. Wearable IoT devices are used by the HIETLM to continuously collect vital data from patients, such as their heart rate, blood pressure, and level of exercise. These pieces of information are sent to a central computer to be processed and analyzed. The suggested model has two main parts: a base model that was learned on a big external dataset, and a transfer learning part that was fine-tuned using data from a single patient. The base model has already been trained on a big set of general health data to learn general trends and traits that are useful for diagnosing CHD. This training helps the model understand complicated patterns and connections that are hard to learn from a small collection. The transfer learning part then tweaks the base model using the patient-specific data to make it fit the unique traits of each patient. To test how well the HIETLM worked, we did tests with a group of patients who were thought to have congenital heart disease. From the data, we can see that the HIETLM is more accurate, sensitive, and detailed than other models. The suggested model could help make CHD diagnoses more accurate and faster, which would improve patient results and lower healthcare costs.