The primary objective of this research is to create a reliable technique to determine whether a patient has glioma, a specific kind of brain tumour, by examining various diagnostic markers, using a variety of machine learning as well as deep learning approaches, and involving XAI (explainable artificial intelligence) methods. Through the integration of patient data, including medical records, genetic profiles, algorithms using machine learning have the ability to predict how each individual will react to different medical interventions. To guarantee regulatory compliance and inspire confidence in AI-driven healthcare solutions, XAI is incorporated. Machine learning methods employed in this study includes Random Forest, decision trees, logistic regression, KNN, Adaboost, SVM, Catboost, LGBM classifier, and Xgboost whereas the deep learning methods include ANN and CNN. Four alternative XAI strategies, including SHAP, Eli5, LIME, and QLattice algorithm, are employed to comprehend the predictions of the model. The Xgboost, a ML model achieved accuracy, precision, recall, f1 score, and AUC of 88%, 82%, 94%, 88%, and 92%, respectively. The best characteristics according to XAI techniques are IDH1, Age at diagnosis, PIK3CA, ATRX, PTEN, CIC, EGFR and TP53. By applying data analytic techniques, the objective is to provide healthcare professionals with practical tool that enhances their capacity for decision-making, enhances resource management, and ultimately raises the bar for patient care. Medical experts can customise treatments and improve patient outcomes by taking into account patient’s particular characteristics. XAI provides justifications to foster faith amongst patients and medical professionals who must rely on AI-assisted diagnosis and treatment recommendations.

Gastrointestinal (GI) diseases are most common worldwide and the death rate can be reduced by early detection. Endoscopy is widely regarded as the gold standard for diagnosing and managing digestive disorders, affecting both the upper and lower GI tracts. Endoscopy is performed to uncover biopsy tissues used to check the presence of cancerous or benign cells, Helicobacter pylori (H. pylori) infection, or perform colonoscopy in case of the removal of polyps. A systematic review was conducted on databases like PubMed, Scopus, Google Scholar, and IEEE Explore, including research papers published up to May 2023, through the systematic search, 33 papers were identified. This review offers valuable insights to physicians and technological guidance to future researchers by examining GI tract diseases. It provides a detailed analysis of Machine learning (ML) techniques like preprocessing, segmentation, feature extraction, and classification. Additionally, Deep Learning (DL) approaches like transfer learning (TL), Convolution Neural Networks (CNN), optimization, transformer, and reinforcement learning have been analyzed for GI diagnosis. The DL approach has increased its use in GI diseases and CNN was the most commonly used architecture. Lastly, the review highlights the research published in the specialized GI fields and provides technological suggestions and insights for future research prospects. Overall, this study broadens the body of knowledge regarding the existing Artificial Intelligence (AI) techniques in gastroenterology as a manual for creating and assessing AI models.

This study elaborates on the design of artificial pancreas using model predictive control algorithm for a comprehensive physiological model such as the Sorensen model, which regulates the blood glucose and can have a longer control time in normal glycaemic region. The main objective of the proposed algorithm is to eliminate the risk of hyper and hypoglycaemia and have a precise infusion of hormones: insulin and glucagon. A single model predictive controller is developed to control the bihormones, insulin, and glucagon for such a development unmeasured disturbance is considered for a random time. The simulation result for the proposed algorithm performed good regulation lowering the hypoglycaemia risk and maintaining the glucose level within the normal glycaemic range. To validate the performance of the tracking of output and setpoint, average tracking error is used and 4.4 mg/dl results are obtained while compared with standard value (14.3 mg/dl).

The main source of power in Electric Vehicles (EVs) is derived from batteries. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. The modeling and simulations were performed using MATLAB/Simulink software. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R₀, R₁ and C₁) model using PowerTrain blockset in MATLAB/Simulink software. To validate the developed model, a NASA dataset was used as the reference dataset. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <±0.2 V. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. This process was performed by tuning the cell parameters. It was found that the cell parameters were independent of the nominal capacity of the cell. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. These parameters showed dependence on SOC and temperature. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A study on SVM technique was carried out for the simulated and tuned data. It is concluded that the SVM algorithm was best suited. A detailed analysis on the errors associated with the algorithms was also carried out.

The complete automated control and delivery of insulin and glucagon in type 1 diabetes is the developing technology for artificial pancreas. This improves the quality of life of a diabetic patient with the precise infusion. The amount of infusion of these hormones is controlled using a control algorithm, which has the prediction property. The control algorithm model predictive control (MPC) predicts one step ahead and infuses the hormones continuously according to the necessity for the regulation of blood glucose. In this research, the authors propose a MPC control algorithm, which is novel for a dual hormone infusion, for a mathematical model such as Sorenson model, and compare it with the insulin alone or single hormone infusion developed with MPC. Since they aim for complete automatic control and regulation, unmeasured disturbances at a random time are integrated and the performance evaluation is projected through statistical analysis. The blood glucose risk index (BGRI) and control variability grid analysis (CVGA) plot gives the additional evaluation for the comparative results of the two controllers claiming 88% performance by dual hormone evaluated through CVGA plot and 2.05 mg/dl average tracking error, 2.20 BGRI. The MPC developed for dual hormone significantly performs better and the time spent in normal glycaemia is longer while eliminating the risk of hyperglycaemia and hypoglycaemia.