Secure and Transparent Land Management with AI and Blockchain

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant. Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.

The demand for high-quality Artificial Intelligence (AI) models is ever-increasing in this digital era. However, most of the existing methods leveraged for managing the ownership, trading, and access of AI models fall short of providing traceability, transparency, audit, security, and trustful features. In this paper, we propose a solution based on blockchain and Non-fungible Tokens (NFTs) to manage ownership rights and exchange of AI models in a transparent, traceable, auditable, secure, and trustworthy manner. Smart contracts are employed to enforce ownership, ease of access, and exchange policies for the unique NFT linked to an AI model. We use decentralized storage of the InterPlanetary File System (IPFS) and proxy re-encryption oracles to securely fetch, store, and share data related to AI models. We present algorithms along with their implementation, testing, and validation details. The proposed solution is evaluated using cost and security analyses to show its affordability and resiliency against security threats and attacks. All smart contract codes are made publicly available on GitHub.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers.

Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

Diabetes is now an important global health problem. This paper presents a patient-oriented electronic health record system for predicting diabetes with machine learning. The diagnosis given by the doctor and the patient ID will be written into the Ethereum Smart Contract. Algorithms analyzed and compared using the Pima Indians Diabetes Dataset are Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and LightGBM. We have also used a decentralized, tamper-proof storage, InterPlanetary File System (IPFS), for storing the patient reports off-chain, and we will store the corresponding hash generated by the SHA-256 cryptographic hash algorithm in the smart contract, having gas optimization as well as scalability. Patients will use the Proof of Stake consensus mechanism-confirmed transparent smart contract transactions to grant or withdraw authorized user access permissions to their health data, with a One-Time Password (OTP) allowing for additional security. In our experimental result, the Support Vector Machine yielded the highest accuracy of 85.06%, f1-score of 0.7928, and a recall of 0.8148, outperforming all other algorithms. This work significantly contributes to privacy preservation by enhancing the existing approaches for diabetes diagnosis in healthcare while keeping the privacy of patient data.

Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.

Prediction of harmful algal blooms in the Persian Gulf using remote sensing and artificial intelligence modeling

Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: An advanced approach toward sustainable and carbon-neutral wastewater treatment.

There is a great health challenge caused by liver disorders in terms of the rising incidence, late diagnosis, and the high number of deaths associated with these conditions. Liver cirrhosis, fatty liver, hepatitis, and liver cancer, among other diseases, do not manifest signs early and hence cannot easily be diagnosed through standard clinical means. The standard approaches used include laboratory and radiologic tests and physicians' expertise. However, they could be too costly and unreliable as well. In recent years, new technologies based on artificial intelligence (AI) have been developed for predicting, diagnosing, and classifying advanced liver disorders. In this research, various techniques based on AI, such as machine learning (ML) and deep learning (DL), will be used for the purpose of detecting liver disorders. Algorithms like decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), and neural networks will be used to extract information from data obtained both clinically and biochemically. The method will be composed of four stages including preprocessing, feature extraction, model training, and performance estimation. Besides, DL models are applied to detect complicated structures in medical data sets. The AI model will aid in early risk detection, classification of diseases, and decision-making in the medical field. The system will be beneficial in helping healthcare experts make more precise and efficient diagnoses while minimizing human errors. Also, AI models will facilitate an automatic analysis of big data for effective predictions about liver disorders. From the findings, it is evident that the use of AI models improves the accuracy of diagnoses when compared to traditional methods. The inclusion of AI in healthcare systems will be advantageous in promoting early interventions, developing personalized treatments, and managing complicated cases of liver disorders. Thus, AI models have great prospects in diagnosing liver conditions. . Keywords: Artificial Intelligence ( AI), Machine Learning ( ML), Deep Learning ( DL), Liver Disorder Detection, Liver Disease Prediction, Clinical Decision Support System ( CDSS)

Accurate evaluation of thyroid nodules is crucial for effective management; however, methods such as ultrasonography and Fine Needle Aspiration Cytology (FNAC) can be subjective and operator-dependent. Indeterminate thyroid nodules (ITNs) complicate diagnosis, coming at the expense of time, money, and potentially additional FNA samplings, causing more discomfort for the patients. Recent advancements in artificial intelligence (AI) assisted ultrasound diagnosis system have demonstrated excellent diagnostic performance and the potential to aid in the differentiation of ITNs. This study aims to develop an AI classifier that integrates the AI-assisted ultrasound diagnosis system, FNAC, and demographic data to enhance the differentiation of benign and malignant thyroid nodules, and to compare the diagnostic performance of the models, with a focus on diagnosing ITNs. In the present research, 620 thyroid nodules were collected from a single medical center and divided into training and testing cohorts (Testing1). We developed five AI models using distinct classification algorithms (Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine) that integrate demographic data, cytological findings, and an AI-assisted ultrasound diagnostic system for thyroid nodule assessment. These models underwent prospective validation (Testing2, n = 243) to identify the optimal model. A subsequent prospective study (Testing3) involving 70 thyroid nodules further evaluated the model's performance, where the selected optimal model was compared against FNAC combined with BRAF V600E mutation analysis. After validation with the Testing1 and Testing2 cohorts, the Random Forest (RF) model demonstrated the best overall performance among the five classifiers. The area under the curve (AUC) for the RF model to diagnose thyroid nodules was 0.994 in the training cohort, 0.993 in the testing cohort, and 0.977 in the prospective data. In addition, for 42 included ITNs in the prospective data, the accuracy, sensitivity, and specificity of the RF model were 90.48%, 89.47%, and 91.30%, respectively. In the Testing 3 cohort, the RF model demonstrated superior diagnostic performance compared to both the standalone AI ultrasound auxiliary diagnostic system and FNAC alone. Its accuracy was comparable to FNAC combined with BRAF V600E mutation analysis. Conclusion: Our developed thyroid nodule AI diagnostic model shows favorable predictive value. It can serve as a decision support tool for non-thyroid specialists and assist thyroid surgeons in the management of ITN. Our developed thyroid nodule AI diagnostic model shows favorable predictive value. It can serve as a decision support tool for non-thyroid specialists and assist thyroid surgeons in the management of ITN.

The present study investigates the synergistic performance of the three-dimensional electrochemical process to decolourise methyl orange (MO) dye pollutant from xenobiotic textile wastewater. The textile dye was treated using electrochemical technique with strong oxidizing potential, and additional adsorption technology was employed to effectively remove dye pollutants from wastewater. Approximately 98% of MO removal efficiency was achieved using 15 mA/cm2 of current density, 3.62 kWh/kg of energy consumption and 79.53% of current efficiency. The 50 mg/L MO pollutant was rapidly mineralized with a half-life of 4.66 min at a current density of 15 mA/cm2. Additionally, graphite intercalation compound (GIC) was electrically polarized in the three-dimensional electrochemical reactor to enhance the direct electrooxidation and.OH generation, thereby improving synergistic treatment efficiency. Decolourisation of MO-polluted wastewater was optimized by artificial intelligence (AI) and machine learning (ML) techniques such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and random forest (RF) algorithms. Statistical metrics indicated the superiority of the model followed this order: ANN > RF > SVM > Multiple regression. The optimization results of the process parameters by artificial neural network (ANN) and random forest (RF) approaches showed that a current density of 15 mA/cm2, electrolysis time of 30 min and initial MO concentration of 50 mg/L were the best operating parameters to maintain current and energy efficiencies of the electrochemical reactor. Finally, Monte Carlo simulations and sensitivity analysis showed that ANN yielded the best prediction efficiency with the lowest uncertainty and variability level, whereas the predictive outcome of random forest was slightly better.Highlights• In-depth analysis of various artificial intelligence optimization techniques.• Prediction efficiency of artificial intelligence and machine learning algorithms.• 98% dye removal and 100% regeneration of graphite intercalation compound.• Advanced statistical analysis of targeted responses and data fitting techniques.• Analysis of uncertainties and variability using Monte Carlo simulation.

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang &amp; Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer &amp; Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen &amp; He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain &amp; Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta &amp; Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

BackgroundGestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis.ObjectiveThis study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used.MethodsAn AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm.ResultsThe areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%.ConclusionsOur prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

Foodborne illnesses caused by harmful bacteria are a major global concern that poses a threat to public health. Traditional detection techniques are frequently time-consuming, labor-intensive, and perhaps inaccurate. The food industry can benefit from the use of artificial intelligence (AI) for the quick, precise, and automated detection of these microorganisms. Foodborne illnesses caused by harmful bacteria are a major public health concern worldwide. Traditional methods for identifying these bacteria are often time-consuming, costly, and may be inaccurate. The food industry can leverage artificial intelligence (AI) to quickly, accurately, and automatically identify these microorganisms. Machine learning (ML) methods such as support vector machines, random forests, and nearest neighbors are used in this context. AI models are capable of identifying pathogens like Salmonella, Listeria, and E. coli with accuracy rates of over 90%. AI facilitates automation and non-destructive testing, which minimizes human labor and errors. Integrating AI with sensor networks makes it possible to monitor food production in real time. The difficulties include securing high-quality datasets, enhancing model transparency, establishing uniform procedures, and securing regulatory approval. Ongoing research aims to solve these problems by developing better data sharing, explainable AI, and standards.