Machine Learning Algorithm

Diseases like diabetes are chronic and require long-term management. Inadequate production of insulin results in high blood sugar levels. Such diseases lead to serious health issues such as heart ailments, blood vessel complaints, eye ailments, kidney function disorders, and nerve ailments. Hence, accurate assessment and management of risk factors are crucial for the onset of diabetes. Our proposed approach combines fuzzy logic & machine learning algorithms for diabetes risk prediction. Three machine learning models were trained to classify patients into two categories of diabetes (Type-I and Type-II) based on their clinical dataset collected from Katihar Medical College & Hospital and Suvadhan Lab. The polynomial regression algorithm achieved a score of 0.947, while the support vector regression algorithm with the rbf kernel achieved a score of 0.954, with a linear kernel achieved a score of 0.73. Our proposed approach performed well with respect to the conventional approaches with improved accuracy by identifying the patients at diabetes risk. In future work, we further analyze the relationship between other ignored factors which contribute to diabetes risk.

Background: Network security and penetration pose a significant challenge in the extensive IoT research of recent years. System security and user privacy demand security solutions that are carefully planned and diligently maintained. Aims: This paper introduces a novel three-stage hybrid IDS, IoT-APOSCA, leveraging machine learning and meta-heuristics for attack detection; stages include pre-processing, feature selection, and attack detection. The pre-processing steps are: cleaning, visualization, feature engineering, and vectorization. Methodology: Networks use Intrusion Detection Systems (IDSs) to monitor and detect malicious activities as a key security feature. The Arctic Puffin Optimization (APO) and Sine-Cosine Algorithm (SCA) are used in the feature selection stage, while a changed Random Neural Network (RNN) is employed in the attack detection stage. Results: The proposed technique is assessed using the DS2OS dataset, and the outcomes show that the approach, integrating multiple learning models, led to an accuracy enhancement to 99.66%. Also, the values Recall and False Alarm Rate obtained are equal to 0.9926 and 0.003, respectively. Conclusion: Intrusion detection system efficacy is directly tied to the quality of its classification method. Enhanced neural network performance is achievable through adjustments to parameters, such as network weights.