Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

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A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniques: Review, taxonomy, research findings, challenges and future directions

In terms of growth, effect, and capability, the 5G-enabled Internet of Things (IoT) is incredible. The volume of data distributed and processed by IoT (Internet of Things) systems that trust connectivity and coverage raises some security problems. As IoT technology is directly used in our daily lives, the threats of present cyberspace may grow more prominent globally. Extended network life, coverage, and connectivity are all required for securing IoT-based 5G network devices. As a result of these failures, there are flaws that lead to security breaches. Because purposeful faults can quickly render the entire network dysfunctional, they are more difficult to identify than unexpected failures. Securing IoT-based 5G Network Device Connectivity and Coverage for expending Encryption and Authentication Scheme (EAS) framework is proposed in this study, which uses novel security flaws. In this research, we proposed a Boltzmann machine (BMKG)-based encryption algorithm for securing 5G-enabled IoT device network environment and compared various asymmetric algorithms for key exchange.

/pdf/securing-5g-iot-device-connectivity-and-coverage-using-49ankdemkj.pdf

Securing 5G-IoT Device Connectivity and Coverage Using Boltzmann Machine Keys Generation

Improving the Protection of Wireless Sensor Network Using a Black Hole Optimization Algorithm (BHOA) on Best Feasible Node Capture Attack

FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks

Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN)

Edge detection is a digital image processing technique to find the boundaries or edges of an image or object through brightness discontinuity. There are many operators to get boundaries or edges but we need more effective and accurate methods. This paper will provide a comparison of hybrid techniques that combine second-order derivative techniques Log and Canny, With Conventional Sobel, Prewitt, Roberts, Canny and Log Operators Edge Detector Techniques With regard to visual inspection, Mean Square Error (MSE), Root Mean Square Error (RMSE), signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean-absolute error (MAE) and Bit error, etc.

Comparative performance analysis of various digital image edge detection techniques with hybrid edge detection technique which is developed by combining second order derivative techniques log and Canny

Soni Changlani

Papers

Implementation of Fruit Fly Optimization Algorithm (FFOA) to escalate the attacking efficiency of node capture attack in Wireless Sensor Networks (WSN)

Comparative performance analysis of various digital image edge detection techniques with hybrid edge detection technique which is developed by combining second order derivative techniques log and Canny