A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin is elastic, round, and stable in a healthy human. This makes it possible to float across red blood cells. But the composition of hemoglobin is unhealthy if you have sickle cell disease. It refers to compact and bent red blood cells. The odd cells obstruct the flow of blood. It is dangerous and can result in severe discomfort, organ damage, heart strokes, and other symptoms. The human life expectancy can be shortened as well. The early identification of sickle calls will help people recognize signs that can assist antibiotics, supplements, blood transfusion, pain-relieving medications, and treatments etc. The manual assessment, diagnosis, and cell count are time consuming process and may result in misclassification and count since millions of red blood cells are in one spell. When utilizing data mining techniques such as the multilayer perceptron classifier algorithm, sickle cells can be effectively detected with high precision in the human body. The proposed approach tackles the limitations of manual research by implementing a powerful and efficient MLP (Multi-Layer Perceptron) classification algorithm that distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Sickle Cells(S) and Thalassemia (T) in red blood cells. This paper also presents the precision degree of the MLP classifier algorithm with other popular mining and machine learning algorithms on the dataset obtained from the Thalassemia and Sickle Cell Society (TSCS) located in Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PDF

Nowadays, people are constantly moving towards various fashion products as a result the e-commerce market for garments is growing rapidly. Online stores must update their features according to user requirements and preferences. However, there are too many options for users to select from these online stores which may leave them in a dilemma to identify the correct outfit, save the user time, and increase sales, efficient recommendation systems are becoming a necessity for online retailers. In this paper, we proposed an Apparel Recommendation System that generates recommendations for users based on their input. We used a real-world data set taken from the online market giant Amazon using Amazon’s Product Advertising API. We aim to use keywords like brand, color, size, etc., to recommend. Data exploration to get detailed information about our dataset, Data Cleaning(pre-processing) to remove invalid sections, Model selection (We have compared different feature extraction techniques like bag of words, TF-IDF, and word2vec model) to find out efficient techniques and Deployment of the model that could facilitate recommendation system to simplify the task of apparel recommendation system. The accuracy of the model is identified using the response time and content matching.

Memory plays a major role in Artificial Neural Networks. Without memory, Neural Network can not be learned itself. One of the primary concepts of memory in neural networks is Associative neural memories. A survey has been made on associative neural memories such as Simple associative memories (SAM), Dynamic associative memories (DAM), Bidirectional Associative memories (BAM), Hopfield memories, Context Sensitive Auto-associative memories (CSAM) and so on. These memories can be applied in various fields to get the effective outcomes. We present a study on these associative memories in artificial neural networks.

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine’s disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine’s health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and KNN performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine’s health.

The state or disorder where the body cannot effectively use the insulin is called Diabetes. If the insulin levels are not maintained properly, the diabetes is one such disorder where it damages all other body parts. It is estimated that the diabetes is the 7th leading cause of deaths as per World Health Organisation report. Early recognition of diabetes, decreases the risk of serious ailments, which includes, heart diseases, brain stroke, eye related diseases, kidney diseases, nerve related diseases etc. In the present work, pima indians diabetes data set is considered as the best dataset and different models viz., hierarchical clustering with decision tree, hierarchical clustering with support vector machines, hierarchical clustering with logistic regression and k means with logistic regression are developed and implemented for identifying and predicting the diabetes. The accuracies of these prediction models range between 0.90 and 0.946. An Improved Diabetes Prediction Algorithm (IDPA) combining the hierarchical clustering algorithm and Naïve Bayes classification algorithm is developed to identify and predict the Type-II diabetes and has shown an accuracy of 0.96. In this IDPA, firstly, the grouping of data into two groups i.e. diabetes and non-diabetes is done by applying the hierarchical clustering algorithm. Then, the filtering is done by comparing the group value to the class value followed by applying Naïve Bayes classification algorithm for predicting diabetes. The results show that the proposed novel method i.e. IDPA can predict the diabetes with higher accuracy levels (0.96) than the traditional/existing methods and other methods which were implemented. This model can be used to predict diabetes early, thereby reducing the serious complications of diabetes.