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Purpose Alzheimer’s is the most commonly occurring neurodegenerative disease and progressive cognitive impairment is its major symptom due to which the patients tend to wander and get lost in unfamiliar places. This is a constant cause of worry for caretakers and a source of distress to the patients themselves. The paper aims to discuss this issue. Design/methodology/approach This paper presents a low-cost, autonomous, embedded systems-based wearable device for real-time location tracking using GPS and the concept of geo-fencing. The system provides real-time updates in the form of a text message sent to the mobile number of a family member or caregiver. Findings An alert is sent whenever the patient moves out of a certain “safe zone” area and sends subsequent updates after every 5 min of such an event. The system supports caregivers of patients with early and moderate Alzheimer’s disease. Social implications Alzheimer’s patients are prone to disorientation, confusion and tend to wander off. Since the device eliminates the need for the patients to operate it and is instead at the discretion of the system itself, the chances of it failing to help are minimized. Hence, with this project, the authors address the need for an autonomous device that can assist caretakers in tracking Alzheimer’s patients. Originality/value The various existing technologies that are in use now for tracking are often high in price, not tailored to Alzheimer’s and are non-autonomous. To overcome this, the authors utilized easily accessible technology into developing this system, which not only be affordable, but also addresses the major flaw in existing systems – which is that they rely on being operated by the patients themselves.

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Abstract. In order to overcome the software development challenges like delivering a project on time`, developing quality software products and reducing development cost, software industries commonly uses defect detection software tools to manage quality in software products. Defects are detected and classified based on their severity, this can be automated in order to reduce the development time and cost. Nowadays to extract useful knowledge from large software repositories engineers and researchers are using data mining techniques. In this paper, software defect detection and classification method is proposed and data mining techniques are integrated to identify, classify the defects from large software repository. Based on defects severity proposed method discussed in this paper focuses on three layers: core, abstraction and application layer. The designed method is evaluated using the parameters precision and recall.

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Agriculture is the main occupation of rural India, which promotes economic growth in the country's development. To increase the yield of the crops to feed the increasing population, it is essential to identify the crops which can be grown in the respective zones. In this article, the Fused Classifier Algorithm (FCA) and Interfused Machine Learning Algorithm (IMLA) are proposed to predict crops suitable for the land based on the zones and agro-climatic parameters. Focusing on the zones of the Karnataka region, the model predicts the crop to the farmers. The different machine learning models such as naïve Bayes, decision tree, neighbors, multilayer perceptron have also been evaluated by varying the hyperparameters and checked for accuracy of the models built. The FCA algorithm merges different algorithms using the error rate with hyperparameters tuning and is given to IMLA to predict crops. This article also compares different machine learning classifiers with the proposed IMLA algorithm, which shows better accuracy with 82.7%.