eural Network (DNN) is now applied in disease prediction to detect various ailments such as heart disease and diabetes. Another disease that is causing a threat to our health is kidney disease. This disease is becoming prevalent due to substances and elements we intake. Ignoring the kidney malfunction can cause chronic kidney disease leading to death. Frequently, Chronic Kidney Disease (CKD) and its symptoms are mild and gradual, often go unnoticed for years only to be realized of late. We conducted our research on CKD in Bade, a Local Government Area of Yobe State in Nigeria. The area has been a center of attention by medical practitioners due to the prevalence of CKD. Unfortunately, a technical approach in culminating the disease is yet to be attained. We obtained a record of 1200 patients with 10 attributes as our dataset from Bade General Hospital and used the DNN model to predict CKD's absence or presence in the patients. The model produced an accuracy of 98%. Furthermore, we identified and highlighted the Features importance to rank the features used in predicting the CKD. The outcome revealed that two attributes: Creatinine and Bicarbonate, have the highest influence on the CKD prediction.

Recent advances in the cutting-edge technologies of biomedical sensing and image processing tools provide us with big data of biomedical and various types of images that can’t be processed within a finite period by professional clinicians. Various techniques for processing biomedical images comprise mathematical algorithms that extract vital diagnostic features from biomedical information and biological data. Because of the complexity and big size of the data computation, intelligence techniques have been applied in processing, visualizing, diagnostic, and classification tasks. This study will explore the effectiveness of the variously artificial intelligence approaches on biomedical signal and image processing applications. The researchers and community entirely will benefit from this study as a guide to the state-of-the-art artificial intelligence techniques for biomedical signal and image processing applications.

Temperature is used to indicate variability and climate changes that indicate the process which is been carried out within the ecosystem and its services. The lack of knowledge about temperature affects human lives in terms of agriculture, transportation, mining, etc. temperature forecasting is used to predict atmospheric conditions based on parameters that caused the temperature to change. This study aims to explore the use of machine learning models for the prediction of temperature, evaluate the performance of these models, and use the model to predict temperature. In this study we explore the use of four different machine learning algorithms for forecasting weather temperature, the algorithms are: Ridge, Random Forest, Linear Regression, and Decision tree. We divided the dataset into training and testing sets, The models were tested on 1000 testing sets based on RMSE score with Decision Tree having the best score of 0.036, Random Forest: 0.208 while Logistic Regression and Ridge had the lowest score of 0.759 respectively.

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