優利公司

Agriculture has certain severe challenges such as inefficient irrigation, late identification of diseases, and inability to monitor the field in real time. To address such issues, there is a unified smart agriculture system that will be proposed in this work through the integration of IoT and ML and DL models. The use of soil moisture, temperature and humidity sensors has facilitated automated irrigation which is capable of sensing the real time conditions in the environment and then turning irrigation on/off to ensure water is used wisely. The combined method of image processing and deep learning algorithms like CNN, ResNet, and YOLO have been utilized in detection of different kinds of diseases in crops as blight, fungal infection, and nutrient deficiencies. The information obtained in the fields with the help of a Raspberry Pi or ESP architecture will trigger irrigation, identify plant diseases, and monitor remotely with the assistance of cloud-hosted platforms, including Thing Speak, Google Drive, and Blynk. Field security is also supplemented by intrusion detection and automatic alert. A unified IoT-ML system enhances irrigation effectiveness, makes diseases recognition over 90 percent, and provides automated scalable service of contemporary precision farming.

Hybrid quantum-classical algorithms can help mitigating the physical limitations of current quantum devices, particularly the low qubit count and the reduced topological connectivity. In this paper, we propose a hybrid technique to solve a well-known NP-hard optimization problem: the Traveling Salesperson Problem (TSP). Our approach is based on a graph contraction technique that removes most of the dimensionality of the original problem instance, producing a sub-TSP of a size suitable to be efficiently solved by a quantum device. The performance of our approach is first demonstrated on classical quantum simulation using Path Integral Monte Carlo, and then run on a D-Wave quantum annealer.

The management of project risks within the multifaceted information technology settings requires predictive structures that can be scaled and are smart enough to model the multidimensional project qualities. This paper presents a machine learning system that uses data analysis to predict the probability of project risks in a multi-class setting based on a structured pipeline comprising data inspection, missing values imputation, outliers' detection using the IQR method, feature engineering, one-hot encoding, feature scaling with StandardScaler, and class balancing with SMOTE. A stacking ensemble framework that combines XGBoost and LightGBM, Gradient Boosting and Random Forest, and CatBoost is used to improve predictive robustness and generalization. Stratified data splitting and cross-validation are used to assess the model and ensure reliable results. The experimental findings on the Project Risk Raw dataset indicate that the proposed Stacking Ensemble achieves an accuracy of 0.9951 on the training set and 0.9334 on the test set, with a precision of 0.9145, a recall of 0.9246, an F1-score of 0.9267, and a standard deviation across cross-validation of 0.0617. Comparative analysis shows performance better than that of ANN, SVM, and CNN, supporting the efficacy of the proposed ensemble-based, intelligent, scalable, and efficient project risk prediction framework.

Artificial intelligence (AI) is increasingly used in drug repurposing to integrate chemical, biological, and clinical data and to prioritize candidate drug–disease associations. Yet current evaluation practices remain dominated by benchmark metrics such as AUROC, AUPRC, F1, or top-k retrieval, which do not by themselves establish pharmacological credibility. A useful prediction is not merely one that scores highly on retrospective datasets, but one that can be connected to a biologically plausible mechanism, safety-relevant context, and a traceable evidentiary basis for experimental follow-up. Mechanistic explainability should therefore be treated as a core objective for translationally oriented AI-enabled repurposing, while also emphasizing that explainability complements rather than replaces experimental validation. Specifically, explanations should connect drugs, targets, pathways, phenotypes, and clinical outcomes in ways that are intelligible to pharmacologists and compatible with experimental prioritization, translational decision-making, and emerging regulatory expectations. We identify a central gap in the literature: many explainability methods emphasize feature attribution or local model transparency, but rarely produce pharmacology-aligned evidence chains that also communicate uncertainty, robustness, and provenance. We introduce biomedical knowledge graphs and neurosymbolic approaches as a promising foundation for more credible repurposing systems because they can support relational inference, structured mechanistic reasoning, and provenance-aware explanation. We further argue that future evaluation should extend beyond predictive accuracy to include mechanistic coherence, uncertainty, robustness, and provenance (MURP) for experimental pharmacology, ideally within workflows that integrate computational prediction with laboratory and, where feasible, real-world validation. Reframing success in this way would improve rigor, translational relevance, and regulatory readiness in AI-driven drug repurposing.

This paper presents the design and implementation of a scalable Enterprise Integration Platform (EIP) built on a modern microservices architecture, utilizing technologies such as ReactJS, Node.js, MongoDB, and Redis. The platform addresses the challenges of integrating heterogeneous enterprise systems by supporting structured and unstructured data via SQL and NoSQL databases. It emphasizes real-time messaging, continuous integration/deployment (CI/CD), and hybrid cloud deployment, ensuring agility and operational efficiency. Performance evaluation demonstrates the platform’s ability to support high throughput and concurrent users, making it suitable for cloud-native, enterprise-grade integration scenarios.