A comparison of artificial intelligence algorithms in diagnosing and predicting gastric cancer: a review study

Today, artificial intelligence is considered a powerful tool that can help physicians identify and diagnose and predict diseases. Gastric cancer has been the fourth most common malignancy and the second leading cause of cancer mortality in the world. Thus, timely diagnosis of this type of cancer could effectively control it. This paper compares AI (artificial intelligence) algorithms in diagnosing and predicting gastric cancer based on types of AI algorithms, sample size, accuracy, sensitivity, and specificity.  This narrative-review paper aims to explore AI algorithms in diagnosing and predicting gastric cancer. To achieve this goal, we reviewed English articles published between 2011 and 2021 in PubMed and Science direct databases. According to the reviews conducted on the published papers, the endoscopic method has been the most used method to collect and incorporate samples into designed models. Also, the SVM (support vector machine), convolutional neural network (CNN), and deep-type CNN have been used the most; therefore, we propose the usage of these algorithms in medical subjects, especially in gastric cancer

Improved point center algorithm for K-Means clustering to increase software defect prediction

The k-means is a clustering algorithm that is often and easy to use. This algorithm is susceptible to randomly chosen centroid points so that it cannot produce optimal results. This research aimed to improve the k-means algorithm’s performance by applying a proposed algorithm called point center. The proposed algorithm overcame the random centroid value in k-means and then applied it to predict software defects modules’ errors. The point center algorithm was proposed to determine the initial centroid value for the k-means algorithm optimization. Then, the selection of X and Y variables determined the cluster center members. The ten datasets were used to perform the testing, of which nine datasets were used for predicting software defects. The proposed center point algorithm showed the lowest errors. It also improved the k-means algorithm’s performance by an average of 12.82% cluster errors in the software compared to the centroid value obtained randomly on the simple k-means algorithm. The findings are beneficial and contribute to developing a clustering model to handle data, such as to predict software defect modules more accurately

Implementing a Hybrid Model using K-Means Clustering and Artificial Neural Networks for Risk Prediction in Life Insurance

Accurate assessment of policy holder risk is critical for life insurance companies to properly price policies and manage long-term liabilities. However, the complexity of risk factors makes reliance solely on traditional actuarial models inadequate, especially with the proliferation of big data and unstandardized data from diverse sources. This study investigated the development and performance of a hybrid machine learning model combining artificial neural networks (ANN) and K-means clustering for enhanced risk prediction in life insurance underwriting. The exponential growth of unlabelled data presented challenges for predictive modelling. The proposed hybrid model leveraged the strengths of artificial neural networks in modelling nonlinear relationships and K-means clustering in unsupervised for pattern recognition to handle unstandardized data. Using anonymized life insurance application data from Kaggle, the hybrid model was evaluated against the artificial neural network algorithm alone. The results demonstrated that integrating K-means clustering and artificial neural network together with principal component analysis for pre-processing led to superior model performance, with testing accuracy improving from 90% for artificial neural network to 98% for the hybrid technique. Additional metrics like precision, recall and AUC also showed enhancements. The improved predictive capability highlighted the potential of the hybrid approach in transforming legacy underwriting practices towards a more sophisticated data-driven analytical evaluation of policy holder risk. However, limitations existed including the use of single sourced insurance dataset due to data privacy concerns. Further research on integrating diverse algorithms can help insurers unlock more value and gain a competitive edge through advanced analytical modelling and testing on larger real-world datasets. While challenges remain, this study provided key insights into a promising new technique for modernizing risk prediction in the life insurance industry in the era of big data

Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan

Perencanaan dari kebutuhan obat-obatan yang tepat dapat membuat pengadaan obat-obatan menjadi efektif dan efisien sehinggaobat-obatan dapat tersedia dengan cukup sesuai dengan kebutuhan serta dapat diperoleh pada saat yang diperlukan. Menganalisa pemakaian obat, perencanaan dan pengendalian obat-obatan dapat dilakukan pada data miningyaitu dengan clusterisasi.Metode yang akan di pakai untuk clustering data obat-obatan adalah algoritma K-Means yang mana merupakan metode clustering dengan non hirarki yang mempartisi data – data  kedalam cluster dimana data – datadengan karakteristik sama akan dikelompokkan padasatu cluster dan data – data dengan karakteristik yang berbeda akan dikelompokkan padacluster lainnya.Tujuan penelitian ini yaitumengelompokkan data obat-obatan pada rumah sakitsehingga dapat digunakan dalam acuan pengambilan keputusan perencanaan dan pengendaliaan persediaan obat-obatan di rumah sakit

Use of agent – based models in characterizing farm types and evolvement in smallholder dairy systems

A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe ever-increasing demand for milk and dairy products has attracted research interventions on how milk yield can be increased for the context of smallholder farmers. While bearing significant contribution on milk production and fulfilment of the market demand, the smallholder dairy farmers are faced with challenges that hinder productivity. Among the challenges is the inadequate characterization of the dairy production systems and lack of knowledge on factors attributing to their growth. This has resulted in aggregation of the smallholder dairy farmers and lack of interventions tailored to suit particular farm types. By using Tanzania and Ethiopia as case studies, this research identified the main determinants for evolvement of smallholder dairy farmers. Evolvement in this research refers to, gradual increase in milk yield. The factors that determine evolvement for individual farm typologies were identified by using cluster and frequent pattern analysis. The differential influence of the identified determinants towards increase in milk yield was studied by using Agent-based modelling and simulation where each factor was observed. Six farm types were identified for Tanzania and four for Ethiopia. The characteristics of the farm types were enriched by frequent pattern analysis with confidence level 60% - 97%. Agentbased modelling revealed that, income and farm-based determinants influenced an increase of up to 7.58 litres above the average (13.62 ± 4.47) for Ethiopia. For Tanzania, farm and farmerbased determinants influenced an increase of up to 7.72 litres of milk above the average (12.7 ± 4.89). The identified determinants could predict up to 96% and 93% of the variances in milk yield for Tanzania and Ethiopia, respectively. There was an increase in milk yield based on the identified evolvement determinants; from baseline data average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. Dairy development agencies should consider the disaggregation of dairy farmers and prioritization of the determinants identified in this research for evolvement of dairy farms. In future, it is important to develop a web or mobile application that can inform smallholder dairy farmers about the identified evolvement determinants to aid on-farm decision making