The Tumor Microenvironment (TME) resists conventional treatments by sending out signals that weaken the immune system. Tumor cells vary significantly, and treatments can lead to resistance. This paper focuses on constructing an effective module for reprogramming the tumor ecosystem using Computational Intelligence-Guided Nanoplatforms. It hypothesizes that computationally optimized fuzzy deep learning, integrated with nanoplatform-mediated drug delivery, can dynamically reprogram tumor-infiltrating T-cells to overcome immunosuppression in the TME, thereby enhancing cytotoxicity and therapeutic response. A novel nanotechnology-integrated deep fuzzy learning framework—Seagull Optimized Sugeno Fuzzy Deep Learning (SgOSF-DL)—is proposed to reprogram T-cell behavior in real-time. Multi-omic data from tumor-infiltrating T-cells are encoded and analyzed using fuzzy logic to determine their immune state (suppressed, exhausted, or active), guided by key biomarkers such as Granzyme B and PD-1. The optimized model governs the release of IL-21 and checkpoint inhibitors via nanoplatforms composed of PLGA, gold nanoshells, and iron oxide particles. Fuzzy rules are formulated using optimized parameters to evaluate the TME. Simulation results confirm that the proposed SgOSF-DL model accurately distinguishes between cancer and healthy cells. It alters cancer behavior by reducing tumor burden, lowering PD-1, and boosting Granzyme B expression. The model achieves 96.5% accuracy in classifying T-cell states, reduces tumor count by 69.2%, and decreases PD-1 expression by 61% for active immune function. It also offers faster therapeutic classification (0.017 seconds) with an activation consistency of 92.8%. Fuzzy logic enables transparent decision-making, aiding clinicians in understanding the treatment rationale.

The Internet of Things (IoT) and machine learning provide insights that would otherwise be hidden in data for quicker, automated responses and improved decision-making. By ingesting images, videos, and audio, machine learning for the Internet of Things can be used to predict future trends, identify anomalies, and enhance intelligence. The IoT organic framework comprises millions of sharp objects, and to form these sharp objects to communicate and work suitably, asset tasks are necessary. Protection of the quality of service (QoS) is one of the diverse reasons that resource tasks ought to be performed. These techniques aid accomplices in choosing tasks resulting in preeminent regard and impact. Prebuilt software-as-a-service (SaaS) applications, called IoT Cleverly applications, can use dashboards to analyze and display data from IoT sensors. If one of the devices is hacked, the security of every other device in this chain is compromised. This can possibly result in second thoughts about a security plans. A user can see key execution indicators and measure the time between data entries by using IoT dashboards and alarms. Calculations based on machine learning can find peculiarities in equipment, notify customers, and even start robotized repairs or proactive countermeasures. AI and Profound Learning resemble managing a real workplace issue such as marking by combining a few innovations that enable constant naming.

“Malignant mesothelioma (MM)” is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is “malignant mesothelioma.” Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination “epithelial/mesenchymal appearance of MM,” however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful “Malignant Mesothelioma illness” identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support “Vector Machine, Neural Network, and Decision Tree” are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.

Data management across servers has grown problematic because of technological advancements in data processing and storage capacities. Data that is neither organized nor labelled adds an additional layer of difficulty to the storing and retrieving processes. This data, which is not tagged, requires analytic techniques that are more powerful and time efficient. Clustering has long been regarded as one of the most effective methods for managing large amounts of data; nonetheless, larger volumes can lead to unexpectedly poor accuracy when using conventional clustering methodologies. In this study, we suggest the use of a novel framework for the clustering of large amounts of data. The preprocessing stage is one of the most important parts in the data cleansing process; hence, a global stop-word list is used to filter the contents of the files before sending them on to the cluster distribution stage. A meta-heuristic focused Genetic Algorithm (GA) is utilized to eradicate the redundant information present in the datasets. In addition to the generalized attributable fitness function, an attribute-based innovative fitness function (f) is being developed. To determine how well proposed method performs, it is compared to a variety of alternative clustering approaches. When comparing the distributions of clusters for the purpose of evaluation, the Standard Error (SE), root mean squared error (RMSE), and corrected R squared error are all computed.