One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

Customer predictive analytics has shown great potential for effective churn models. Thriving in today's telecommunications industry, discerning between consumers who are likely to migrate to a competitor is enormous. Having reliable predictive client behavior in the future is required. Machine learning algorithms are essential to predict customer turnovers, and researchers have proposed various techniques. Churn prediction is a problem due to the unequal dispersal of classes. Most traditional machine learning algorithms are ineffective in classifying data. Client cluster with a higher risk has been discovered. A support vector machine is employed as the foundational learner, and a churn prediction model is constructed based on each analysis of variance. The separation of churn data revealed by experimental assessment is recommended for churn prediction analysis. Customer attrition is high, but an instantaneous support can ensure that customer needs are addressed and assess an employee's capacity to achieve customer satisfaction. This study uses an analysis of variance (ANOVA) with a support vector machine (SVM), classification in analyzing risks in telecom systems It may be determined that SVM provides the most accurate forecast of customer turnover (95%). The projected outcomes will allow other organizations to assess possible client turnover and collect customer feedback.

Face recognition has been an active research area in the pattern recognition and computer vision domains due to its many potential applications in surveillance, credit cards, passport and security. However, the problem of correct method of partitioning the face data into train and test set has always been a challenge to the development of a robust face recognition system. The performance of the System was tested on locally acquired face database when the face database was randomly partitioned and when k-fold Cross Validation partition was used. The face database was captured under the condition of significant variations of rotation, illumination and facial expression. Quantitative evaluation experimental results showed that Random Sampling technique has a higher average recognition rate (96.7%) than Cross Validation partition method (95.3%). However, recognition time in Cross Validation is faster (0.36 secs) than that of Random Sampling (0.38 secs).

In precision agriculture, artificial intelligence (AI) has become a game-changing technology that allows farmers to maximize crop management and boost output. Recently, developments in machine learning and AI have opened up new opportunities for precision agriculture, particularly in the field of crop management. Early disease identification is made possible by computer vision and machine learning techniques, which also enable tailored interventions to reduce yield losses. Farmers can receive personalized advice on the bestpractices for planting, irrigation, and fertilizer management via chatbots and advisory services powered by AI. Artificial intelligence algorithms combined with sensor networks allow for just-in-time decision making by providing real-time weather, plant health, and soil monitoring. Despite the well-established advantages of AI in precision agriculture, there are still difficulties which calls for continual research. This work presents a narrative of the most recent developments in the use of AI in agricultural farming across a range of domains in Nigeria. This was done by reviewing and identifying the AI methodological approach used in allied research carried out for crop management. Appraisal of the researches, AI technique employed, contributions and milestones in this domain, amassed in the literature reveals plethora of obstacles encompassing restricted data accessibility, interpretability of models, and implementation inresource-constrained settings amongst others. It is therefore recommended that critical attention be given to address these restrictions so as to expand the potential of AI-driven solutions and attaining precision agriculture's full promise for global food security.