University of the Potomac

Diabetes mellitus is a chronic metabolic disease that is a significant global public health concern. Complications may potentially be avoided or postponed with early diabetes diagnosis and treatment. The development of ML and DL has created new possibilities in the analysis of clinical data, allowing to identify the pattern concealed in them and increase the accuracy of diagnosis. This research proposes a diabetes prediction model based on the PIMA Indian Diabetes Dataset through the application of the Machine Learning (ML) and Deep Learning (DL) methods. Random Forest (RF) and Long Short-term Memory (LSTM) are two high-performance models that were implemented to extract nonlinear and temporal relationships in the data. Experimental testing showed that RF had 97.54% accuracy and 96.32 F1-score, whereas LSTM had a steady 98.85% accuracy and 98.20 F1-score. The proposed RF and LSTM models showed a clear superiority when compared to more traditional models, like Naive Bayes, Decision Trees, AdaBoost, and SVM (72-79% accuracy), to the more advanced models, like ANN, CNN-LSTM, and DNN (89-93% accuracy). The proposed framework demonstrates that combining ensemble and sequential learning offers a scalable and accurate solution for early diabetes detection, representing a key contribution to clinical decision support.

The digital revolution in procurement has turned out to be an essential facilitator towards operational effectiveness, cost minimization and strategic decision-making in contemporary businesses. The conventional procurement systems are usually associated with manual operations, disintegrated systems of data, and weak analytical tools, which create inefficiencies and slow down the decision-making process. In order to overcome these issues, this paper suggests a smart automation-based system of digital procurement change incorporating such innovative technologies as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and data analytics. The suggested solution will allow complete automation of the procurement processes, such as the selection of suppliers, demand forecasting, contract management, and real-time decision support. The framework improves procurement visibility, mitigation of risks, and the evaluation of supplier performance with the help of predictive analytics and intelligent agents. Moreover, the system has adaptive learning systems so that it keeps enhancing the accuracy of decisions and operational robustness in dynamic market conditions. The results of experimental assessment indicate that there are huge differences in the time of procurement cycle, saved costs, and efficiency of the decisionmaking process, which are better than the traditional approaches. The suggested model offers an upscaled and customizable approach to companies that intend to shift to datadriven, intelligent procurement ecosystems, which would allow building sustainable and competitive businesses.

Caregiving is a road that most individuals take at some time in their life. Informal caregivers, for instance, are in charge of looking after their loved ones. However, some caregivers extend these duties for extended periods of time while providing care for loved ones who have been diagnosed with a variety of chronic illnesses. It may be rewarding and difficult to provide informal care to those who are depressed. Caretakers may have financial, emotional, or physical health challenges in addition to losses like lost wages, reduced health coverage, and decreased retirement funds. In some circumstances, caregivers could even be compelled to quit their employment or reduce their hours. This study is essential given the growing number of people with mental health conditions and the increased demand for caregivers. An empirical review of management techniques used by caretakers for individuals with mental health conditions is presented in this article. According to this study, those with uterine fibroids, cancer, heart illness, diabetes, epilepsy, and physical or mental disabilities are considered mental patients. Future caregivers will find it easier and more beneficial to have a wide awareness of the topic.

Distributed Denial of Service (DDoS) is found to be the most disruptive threat for the current network infrastructures, especially with the advent of cloud computing, Internet of Things (IoT), and Software-Defined Networking (SDN). The detection methods traditionally used fail to effectively counter the sophistication of DDoS attacks. The recent developments in machine learning (ML) and deep learning (DL) algorithms have greatly impacted the detection of DDoS attacks, as these algorithms can well identify the complex, nonlinear behavior of DDoS attacks. Class imbalance has been reported to affect the detection of DDoS attacks by contemporary detection systems. Then we propose an innovative hybrid model that uses Artificial Neural Networks (ANN) and Decision Trees (DT). The proposed hybrid model has achieved an impressive accuracy of 98.77% on the CICDDoS2019 data set, with precision, recall, and F1-score values of 98%, 99%, and 98.5%, respectively, outperforming the traditional detection methods. The presented hybrid model has effectively addressed the problem of class imbalance, providing an effective solution for the detection of DDoS attacks.

Test-time adaptation (TTA) can improve predictions after deployment, but the update itself can become a new failure mode when unlabeled streams are transient, contaminated, imbalanced, or recurrent. We introduce TRUST-ADAPT, a risk-gated TTA algorithm that treats adaptation as a reversible intervention rather than a default response. A frozen source model converts confidence nonconformity into split-conformal p-values; a batch-level Hoeffding gate triggers learning only when the fraction of anomalous samples exceeds a finite-sample threshold. Triggered batches are further filtered by predictive uncertainty, adapted with pseudo-label and entropy objectives regularized by an immutable source anchor and selective class-balanced memory, and accepted only if an anchor-risk validation check passes; otherwise the parameters are rolled back. Under super-uniform conformal p-values and conditional within-batch independence, the gate controls the probability of a false adaptation trigger at a user-specified level. We implement the complete procedure in PyTorch and execute a locked five-seed controlled benchmark on streaming digit classification with abrupt, recurring, and 15% contaminated shifts. Across 15 seed-scenario pairs, TRUST-ADAPT improves mean streaming accuracy over the frozen source by 3.20 percentage points (Holm-adjusted Wilcoxon p=0.0039) while using 49.8 update batches on average versus 90.0 for TENT-style adaptation. The method is particularly competitive under contamination, but recurring shifts remain a limitation. All reported values are generated by the accompanying executable code; no unexecuted numerical result is claimed.